diff --git a/python/sandbox.ipynb b/python/sandbox.ipynb index 8e60145..40c5f81 100644 --- a/python/sandbox.ipynb +++ b/python/sandbox.ipynb @@ -3,7 +3,7 @@ { "cell_type": "code", "execution_count": 51, - "id": "7de7295a", + "id": "87dd4fb9", "metadata": { "ExecuteTime": { "end_time": "2024-04-10T12:37:25.098785Z", @@ -28,7 +28,7 @@ { "cell_type": "code", "execution_count": 179, - "id": "e244d137", + "id": "ab35c09e", "metadata": { "ExecuteTime": { "end_time": "2024-04-11T04:19:40.595606Z", @@ -46,7 +46,7 @@ { "cell_type": "code", "execution_count": 53, - "id": "c49b53cd", + "id": "6a76823c", "metadata": { "ExecuteTime": { "end_time": "2024-04-10T12:37:27.663173Z", @@ -60,16 +60,17 @@ }, { "cell_type": "code", - "execution_count": 123, - "id": "baa1e06a", + "execution_count": 208, + "id": "29b09a91", "metadata": { "ExecuteTime": { - "end_time": "2024-04-10T12:56:54.870854Z", - "start_time": "2024-04-10T12:56:54.764155Z" + "end_time": "2024-04-11T11:24:47.119855Z", + "start_time": "2024-04-11T11:24:47.063798Z" } }, "outputs": [], "source": [ + "from palin.simulation.experiment import Experiment\n", "from palin.simulation.simple_experiment import SimpleExperiment\n", "from palin.simulation.double_pass_experiment import DoublePassExperiment\n", "from palin.simulation.trial import Int2Trial, Int1Trial \n", @@ -84,7 +85,7 @@ }, { "cell_type": "markdown", - "id": "fa2978b0", + "id": "31bb6aee", "metadata": {}, "source": [ "## Simulate with internal noise" @@ -92,7 +93,7 @@ }, { "cell_type": "markdown", - "id": "bd21029c", + "id": "cd63729b", "metadata": {}, "source": [ "Single run" @@ -100,30 +101,22 @@ }, { "cell_type": "code", - "execution_count": 122, - "id": "2b2b91ae", + "execution_count": 189, + "id": "63e53f77", "metadata": { "ExecuteTime": { - "end_time": "2024-04-10T12:42:09.804159Z", - "start_time": "2024-04-10T12:42:09.665497Z" + "end_time": "2024-04-11T04:53:29.899499Z", + "start_time": "2024-04-11T04:53:29.371264Z" } }, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "E:\\WORK\\DO\\2022\\palin\\python\\palin\\internal_noise\\double_pass.py:43: FutureWarning: Index.ravel returning ndarray is deprecated; in a future version this will return a view on self.\n", - " pass_count_df.columns = [\"_\".join(x) for x in pass_count_df.columns.ravel()]\n" - ] - }, { "data": { "text/plain": [ - "0.24" + "(0.1, 1)" ] }, - "execution_count": 122, + "execution_count": 189, "metadata": {}, "output_type": "execute_result" } @@ -138,13 +131,13 @@ " internal_noise_std = 0, \n", " criteria = 1)\n", "responses = obs.respond_to_experiment(exp)\n", - "ana = InternalNoiseValue(internal_noise_extractor = DoublePass)\n", + "ana = InternalNoiseValue(internal_noise_extractor = DoublePass, model_file='model.csv')\n", "ana.analyse(exp, obs, responses)" ] }, { "cell_type": "markdown", - "id": "6396b632", + "id": "d90d2366", "metadata": {}, "source": [ "Simulation" @@ -152,11 +145,12 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "21b866b8", + "execution_count": 200, + "id": "fc511049", "metadata": { "ExecuteTime": { - "start_time": "2024-04-11T04:20:32.079Z" + "end_time": "2024-04-11T06:57:34.621971Z", + "start_time": "2024-04-11T06:00:58.596353Z" }, "scrolled": false }, @@ -166,7 +160,7 @@ "output_type": "stream", "text": [ "Running 1 configs\n", - "{'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 100, 'kernel': [1], 'internal_noise_std': 0, 'criteria': 0, 'internal_noise_extractor': , 'model_file': 'model_11_04_2024.csv', 'rebuild_model': False, 'internal_noise_range': array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. , 1.1, 1.2,\n", + "{'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 100, 'kernel': [1], 'internal_noise_std': 0, 'criteria': 0, 'internal_noise_extractor': , 'model_file': 'model_large.csv', 'rebuild_model': False, 'internal_noise_range': array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. , 1.1, 1.2,\n", " 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2. , 2.1, 2.2, 2.3, 2.4, 2.5,\n", " 2.6, 2.7, 2.8, 2.9, 3. , 3.1, 3.2, 3.3, 3.4, 3.5, 3.6, 3.7, 3.8,\n", " 3.9, 4. , 4.1, 4.2, 4.3, 4.4, 4.5, 4.6, 4.7, 4.8, 4.9, 5. ]), 'criteria_range': array([-5.00000000e+00, -4.90000000e+00, -4.80000000e+00, -4.70000000e+00,\n", @@ -193,9 +187,55 @@ " 3.40000000e+00, 3.50000000e+00, 3.60000000e+00, 3.70000000e+00,\n", " 3.80000000e+00, 3.90000000e+00, 4.00000000e+00, 4.10000000e+00,\n", " 4.20000000e+00, 4.30000000e+00, 4.40000000e+00, 4.50000000e+00,\n", - " 4.60000000e+00, 4.70000000e+00, 4.80000000e+00, 4.90000000e+00])}\n", + " 4.60000000e+00, 4.70000000e+00, 4.80000000e+00, 4.90000000e+00]), 'n_runs': 2}\n", ".Rebuilding double-pass model\n" ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[200], line 19\u001b[0m\n\u001b[0;32m 9\u001b[0m analyser_params \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m'\u001b[39m\u001b[38;5;124minternal_noise_extractor\u001b[39m\u001b[38;5;124m'\u001b[39m:[DoublePass], \n\u001b[0;32m 10\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmodel_file\u001b[39m\u001b[38;5;124m'\u001b[39m: [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmodel_large.csv\u001b[39m\u001b[38;5;124m'\u001b[39m], \n\u001b[0;32m 11\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrebuild_model\u001b[39m\u001b[38;5;124m'\u001b[39m: [\u001b[38;5;28;01mFalse\u001b[39;00m],\n\u001b[0;32m 12\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124minternal_noise_range\u001b[39m\u001b[38;5;124m'\u001b[39m:[np\u001b[38;5;241m.\u001b[39marange(\u001b[38;5;241m0\u001b[39m,\u001b[38;5;241m5.1\u001b[39m,\u001b[38;5;241m0.1\u001b[39m)],\n\u001b[0;32m 13\u001b[0m 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\u001b[1;32mE:\\WORK\\DO\\2022\\palin\\python\\palin\\simulation\\simulation.py:45\u001b[0m, in \u001b[0;36mSimulation.run_all\u001b[1;34m(self, n_runs, verbose)\u001b[0m\n\u001b[0;32m 43\u001b[0m run_res \u001b[38;5;241m=\u001b[39m config_param\u001b[38;5;241m.\u001b[39mcopy() \n\u001b[0;32m 44\u001b[0m run_res\u001b[38;5;241m.\u001b[39mupdate({\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrun\u001b[39m\u001b[38;5;124m'\u001b[39m:run}) \n\u001b[1;32m---> 45\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mconfig_param\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 46\u001b[0m run_res\u001b[38;5;241m.\u001b[39mupdate(results)\n\u001b[0;32m 47\u001b[0m runs\u001b[38;5;241m.\u001b[39mappend(run_res)\n", + "File \u001b[1;32mE:\\WORK\\DO\\2022\\palin\\python\\palin\\simulation\\simulation.py:67\u001b[0m, in \u001b[0;36mSimulation.run\u001b[1;34m(self, config_param)\u001b[0m\n\u001b[0;32m 64\u001b[0m responses \u001b[38;5;241m=\u001b[39m obs\u001b[38;5;241m.\u001b[39mrespond_to_experiment(exp)\n\u001b[0;32m 66\u001b[0m metrics \u001b[38;5;241m=\u001b[39m ana\u001b[38;5;241m.\u001b[39mget_metric_names()\n\u001b[1;32m---> 67\u001b[0m values \u001b[38;5;241m=\u001b[39m \u001b[43mana\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43manalyse\u001b[49m\u001b[43m(\u001b[49m\u001b[43mexp\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mobs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mresponses\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 69\u001b[0m \u001b[38;5;66;03m# return the metrics as a dict of name:value pairs\u001b[39;00m\n\u001b[0;32m 70\u001b[0m results \u001b[38;5;241m=\u001b[39m {}\n", + "File \u001b[1;32mE:\\WORK\\DO\\2022\\palin\\python\\palin\\simulation\\internal_noise_value.py:21\u001b[0m, in \u001b[0;36mInternalNoiseValue.analyse\u001b[1;34m(self, experiment, participant, participant_responses)\u001b[0m\n\u001b[0;32m 19\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21manalyse\u001b[39m(\u001b[38;5;28mself\u001b[39m, experiment, participant, participant_responses): \n\u001b[1;32m---> 21\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mestimate_internal_noise\u001b[49m\u001b[43m(\u001b[49m\u001b[43mexperiment\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparticipant_responses\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[1;32mE:\\WORK\\DO\\2022\\palin\\python\\palin\\simulation\\internal_noise_value.py:27\u001b[0m, in \u001b[0;36mInternalNoiseValue.estimate_internal_noise\u001b[1;34m(self, experiment, participant_responses)\u001b[0m\n\u001b[0;32m 23\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mestimate_internal_noise\u001b[39m(\u001b[38;5;28mself\u001b[39m, experiment, participant_responses): \n\u001b[0;32m 25\u001b[0m responses_df \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mto_df(experiment, participant_responses)\n\u001b[1;32m---> 27\u001b[0m internal_noise, criteria \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minternal_noise_extractor\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mextract_single_internal_noise\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata_df\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mresponses_df\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 28\u001b[0m \u001b[43m \u001b[49m\u001b[43mtrial_id\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mtrial\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstim_id\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mstim\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfeature_id\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mfeature\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue_id\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mvalue\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mresponse_id\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mresponse\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel_file\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel_file\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 29\u001b[0m \u001b[43m \u001b[49m\u001b[43minternal_noise_range\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minternal_noise_range\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcriteria_range\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcriteria_range\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn_repeated_trials\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mn_repeated_trials\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn_runs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mn_runs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 31\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m internal_noise, criteria\n", + "File \u001b[1;32mE:\\WORK\\DO\\2022\\palin\\python\\palin\\internal_noise\\double_pass.py:46\u001b[0m, in \u001b[0;36mDoublePass.extract_single_internal_noise\u001b[1;34m(cls, data_df, trial_id, stim_id, feature_id, value_id, response_id, model_file, rebuild_model, internal_noise_range, criteria_range, n_repeated_trials, n_runs)\u001b[0m\n\u001b[0;32m 43\u001b[0m \u001b[38;5;66;03m# compute probability of choosing first response option\u001b[39;00m\n\u001b[0;32m 44\u001b[0m prob_first \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39mcompute_prob_first(data_df, trial_id\u001b[38;5;241m=\u001b[39mtrial_id, response_id\u001b[38;5;241m=\u001b[39mresponse_id, stim_id\u001b[38;5;241m=\u001b[39mstim_id, double_pass_id\u001b[38;5;241m=\u001b[39mdouble_pass_id)\n\u001b[1;32m---> 46\u001b[0m internal_noise, criteria \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mestimate_noise_criteria\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprob_agree\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprob_first\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel_file\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrebuild_model\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minternal_noise_range\u001b[49m\u001b[43m,\u001b[49m\u001b[43mcriteria_range\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn_repeated_trials\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn_runs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 48\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m internal_noise,criteria\n", + "File \u001b[1;32mE:\\WORK\\DO\\2022\\palin\\python\\palin\\internal_noise\\double_pass.py:122\u001b[0m, in \u001b[0;36mDoublePass.estimate_noise_criteria\u001b[1;34m(cls, prob_agree, prob_first, model_file, rebuild_model, internal_noise_range, criteria_range, n_repeated_trials, n_runs)\u001b[0m\n\u001b[0;32m 120\u001b[0m model_df \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mread_csv(model_file, index_col\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m)\n\u001b[0;32m 121\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 122\u001b[0m model_df \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbuild_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43minternal_noise_range\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcriteria_range\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn_repeated_trials\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn_runs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 123\u001b[0m model_df\u001b[38;5;241m.\u001b[39mto_csv(model_file)\n\u001b[0;32m 125\u001b[0m \u001b[38;5;66;03m# find internal_noise & criteria settings that minimizes distance to prob_agree and prob_first \u001b[39;00m\n", + "File \u001b[1;32mE:\\WORK\\DO\\2022\\palin\\python\\palin\\internal_noise\\double_pass.py:154\u001b[0m, in \u001b[0;36mDoublePass.build_model\u001b[1;34m(cls, internal_noise_range, criteria_range, n_repeated_trials, n_runs)\u001b[0m\n\u001b[0;32m 148\u001b[0m analyser_params \u001b[38;5;241m=\u001b[39m {}\n\u001b[0;32m 150\u001b[0m sim \u001b[38;5;241m=\u001b[39m Sim(DoublePassExperiment, experiment_params,\n\u001b[0;32m 151\u001b[0m LinearObserver, observer_params, \n\u001b[0;32m 152\u001b[0m DoublePassStatistics, analyser_params)\n\u001b[1;32m--> 154\u001b[0m sim_df \u001b[38;5;241m=\u001b[39m \u001b[43msim\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_all\u001b[49m\u001b[43m(\u001b[49m\u001b[43mn_runs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mn_runs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mverbose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[0;32m 156\u001b[0m \u001b[38;5;66;03m# average measures over all runs\u001b[39;00m\n\u001b[0;32m 157\u001b[0m sim_df\u001b[38;5;241m.\u001b[39mgroupby([\u001b[38;5;124m'\u001b[39m\u001b[38;5;124minternal_noise_std\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcriteria\u001b[39m\u001b[38;5;124m'\u001b[39m])[DoublePassStatistics\u001b[38;5;241m.\u001b[39mget_metric_names]\u001b[38;5;241m.\u001b[39mmean()\n", + "File \u001b[1;32mE:\\WORK\\DO\\2022\\palin\\python\\palin\\simulation\\simulation.py:45\u001b[0m, in \u001b[0;36mSimulation.run_all\u001b[1;34m(self, n_runs, verbose)\u001b[0m\n\u001b[0;32m 43\u001b[0m run_res \u001b[38;5;241m=\u001b[39m config_param\u001b[38;5;241m.\u001b[39mcopy() \n\u001b[0;32m 44\u001b[0m run_res\u001b[38;5;241m.\u001b[39mupdate({\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrun\u001b[39m\u001b[38;5;124m'\u001b[39m:run}) \n\u001b[1;32m---> 45\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mconfig_param\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 46\u001b[0m run_res\u001b[38;5;241m.\u001b[39mupdate(results)\n\u001b[0;32m 47\u001b[0m runs\u001b[38;5;241m.\u001b[39mappend(run_res)\n", + "File \u001b[1;32mE:\\WORK\\DO\\2022\\palin\\python\\palin\\simulation\\simulation.py:67\u001b[0m, in \u001b[0;36mSimulation.run\u001b[1;34m(self, config_param)\u001b[0m\n\u001b[0;32m 64\u001b[0m responses \u001b[38;5;241m=\u001b[39m obs\u001b[38;5;241m.\u001b[39mrespond_to_experiment(exp)\n\u001b[0;32m 66\u001b[0m metrics \u001b[38;5;241m=\u001b[39m ana\u001b[38;5;241m.\u001b[39mget_metric_names()\n\u001b[1;32m---> 67\u001b[0m values \u001b[38;5;241m=\u001b[39m \u001b[43mana\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43manalyse\u001b[49m\u001b[43m(\u001b[49m\u001b[43mexp\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mobs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mresponses\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 69\u001b[0m \u001b[38;5;66;03m# return the metrics as a dict of name:value pairs\u001b[39;00m\n\u001b[0;32m 70\u001b[0m results \u001b[38;5;241m=\u001b[39m {}\n", + "File \u001b[1;32mE:\\WORK\\DO\\2022\\palin\\python\\palin\\simulation\\double_pass_statistics.py:19\u001b[0m, in \u001b[0;36mDoublePassStatistics.analyse\u001b[1;34m(self, experiment, participant, participant_responses)\u001b[0m\n\u001b[0;32m 15\u001b[0m responses_df \u001b[38;5;241m=\u001b[39m DoublePass\u001b[38;5;241m.\u001b[39mindex_double_pass_trials(data_df \u001b[38;5;241m=\u001b[39m responses_df, \n\u001b[0;32m 16\u001b[0m trial_id\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtrial\u001b[39m\u001b[38;5;124m'\u001b[39m,value_id\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mvalue\u001b[39m\u001b[38;5;124m'\u001b[39m, double_pass_id\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdouble_pass_id\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m 18\u001b[0m \u001b[38;5;66;03m# compute probability of agreement over double pass\u001b[39;00m\n\u001b[1;32m---> 19\u001b[0m prob_agree \u001b[38;5;241m=\u001b[39m \u001b[43mDoublePass\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcompute_prob_agreement\u001b[49m\u001b[43m(\u001b[49m\u001b[43mresponses_df\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrial_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mtrial\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mresponse_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mresponse\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdouble_pass_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mdouble_pass_id\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m 20\u001b[0m \u001b[38;5;66;03m# compute probability of choosing first response option\u001b[39;00m\n\u001b[0;32m 21\u001b[0m prob_first \u001b[38;5;241m=\u001b[39m DoublePass\u001b[38;5;241m.\u001b[39mcompute_prob_first(responses_df, trial_id\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtrial\u001b[39m\u001b[38;5;124m'\u001b[39m, response_id\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mresponse\u001b[39m\u001b[38;5;124m'\u001b[39m, stim_id\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mstim\u001b[39m\u001b[38;5;124m'\u001b[39m, double_pass_id\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdouble_pass_id\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", + "File \u001b[1;32mE:\\WORK\\DO\\2022\\palin\\python\\palin\\internal_noise\\double_pass.py:92\u001b[0m, in \u001b[0;36mDoublePass.compute_prob_agreement\u001b[1;34m(cls, data_df, trial_id, response_id, double_pass_id)\u001b[0m\n\u001b[0;32m 90\u001b[0m d \u001b[38;5;241m=\u001b[39m group\u001b[38;5;241m.\u001b[39mgroupby(trial_id)\u001b[38;5;241m.\u001b[39magg({response_id: \u001b[38;5;28;01mlambda\u001b[39;00m group: \u001b[38;5;28mtuple\u001b[39m(group)})\u001b[38;5;241m.\u001b[39mreset_index()\n\u001b[0;32m 91\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m d\u001b[38;5;241m.\u001b[39mresponse\u001b[38;5;241m.\u001b[39mnunique()\u001b[38;5;241m==\u001b[39m\u001b[38;5;241m1\u001b[39m\n\u001b[1;32m---> 92\u001b[0m agrees \u001b[38;5;241m=\u001b[39m \u001b[43mdata_df\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgroupby\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdouble_pass_id\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43;01mlambda\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mgroup\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43msame_answer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgroup\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrial_id\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mresponse_id\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 94\u001b[0m \u001b[38;5;66;03m# return agreement probability\u001b[39;00m\n\u001b[0;32m 95\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m agrees\u001b[38;5;241m.\u001b[39msum()\u001b[38;5;241m/\u001b[39m\u001b[38;5;28mlen\u001b[39m(agrees)\n", + "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\core\\groupby\\groupby.py:1567\u001b[0m, in \u001b[0;36mGroupBy.apply\u001b[1;34m(self, func, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1559\u001b[0m new_msg \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m 1560\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe operation \u001b[39m\u001b[38;5;132;01m{\u001b[39;00morig_func\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m failed on a column. If any error is \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 1561\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mraised, this will raise an exception in a future version \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 1562\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mof pandas. Drop these columns to avoid this warning.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 1563\u001b[0m )\n\u001b[0;32m 1564\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m rewrite_warning(\n\u001b[0;32m 1565\u001b[0m old_msg, \u001b[38;5;167;01mFutureWarning\u001b[39;00m, new_msg\n\u001b[0;32m 1566\u001b[0m ) \u001b[38;5;28;01mif\u001b[39;00m is_np_func \u001b[38;5;28;01melse\u001b[39;00m nullcontext():\n\u001b[1;32m-> 1567\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_python_apply_general\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_selected_obj\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1568\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[0;32m 1569\u001b[0m \u001b[38;5;66;03m# gh-20949\u001b[39;00m\n\u001b[0;32m 1570\u001b[0m \u001b[38;5;66;03m# try again, with .apply acting as a filtering\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 1574\u001b[0m \u001b[38;5;66;03m# fails on *some* columns, e.g. a numeric operation\u001b[39;00m\n\u001b[0;32m 1575\u001b[0m \u001b[38;5;66;03m# on a string grouper column\u001b[39;00m\n\u001b[0;32m 1577\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_group_selection_context():\n\u001b[0;32m 1578\u001b[0m \u001b[38;5;66;03m# GH#50538\u001b[39;00m\n", + "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\core\\groupby\\groupby.py:1629\u001b[0m, in \u001b[0;36mGroupBy._python_apply_general\u001b[1;34m(self, f, data, not_indexed_same, is_transform, is_agg)\u001b[0m\n\u001b[0;32m 1592\u001b[0m \u001b[38;5;129m@final\u001b[39m\n\u001b[0;32m 1593\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_python_apply_general\u001b[39m(\n\u001b[0;32m 1594\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 1599\u001b[0m is_agg: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[0;32m 1600\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m NDFrameT:\n\u001b[0;32m 1601\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 1602\u001b[0m \u001b[38;5;124;03m Apply function f in python space\u001b[39;00m\n\u001b[0;32m 1603\u001b[0m \n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 1627\u001b[0m \u001b[38;5;124;03m data after applying f\u001b[39;00m\n\u001b[0;32m 1628\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m-> 1629\u001b[0m values, mutated \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgrouper\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1630\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m not_indexed_same \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 1631\u001b[0m not_indexed_same \u001b[38;5;241m=\u001b[39m mutated \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmutated\n", + "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\core\\groupby\\ops.py:839\u001b[0m, in \u001b[0;36mBaseGrouper.apply\u001b[1;34m(self, f, data, axis)\u001b[0m\n\u001b[0;32m 837\u001b[0m \u001b[38;5;66;03m# group might be modified\u001b[39;00m\n\u001b[0;32m 838\u001b[0m group_axes \u001b[38;5;241m=\u001b[39m group\u001b[38;5;241m.\u001b[39maxes\n\u001b[1;32m--> 839\u001b[0m res \u001b[38;5;241m=\u001b[39m \u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgroup\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 840\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m mutated \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m _is_indexed_like(res, group_axes, axis):\n\u001b[0;32m 841\u001b[0m mutated \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n", + "File \u001b[1;32mE:\\WORK\\DO\\2022\\palin\\python\\palin\\internal_noise\\double_pass.py:92\u001b[0m, in \u001b[0;36mDoublePass.compute_prob_agreement..\u001b[1;34m(group)\u001b[0m\n\u001b[0;32m 90\u001b[0m d \u001b[38;5;241m=\u001b[39m group\u001b[38;5;241m.\u001b[39mgroupby(trial_id)\u001b[38;5;241m.\u001b[39magg({response_id: \u001b[38;5;28;01mlambda\u001b[39;00m group: \u001b[38;5;28mtuple\u001b[39m(group)})\u001b[38;5;241m.\u001b[39mreset_index()\n\u001b[0;32m 91\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m d\u001b[38;5;241m.\u001b[39mresponse\u001b[38;5;241m.\u001b[39mnunique()\u001b[38;5;241m==\u001b[39m\u001b[38;5;241m1\u001b[39m\n\u001b[1;32m---> 92\u001b[0m agrees \u001b[38;5;241m=\u001b[39m data_df\u001b[38;5;241m.\u001b[39mgroupby(double_pass_id)\u001b[38;5;241m.\u001b[39mapply(\u001b[38;5;28;01mlambda\u001b[39;00m group: \u001b[43msame_answer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgroup\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrial_id\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mresponse_id\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[0;32m 94\u001b[0m \u001b[38;5;66;03m# return agreement probability\u001b[39;00m\n\u001b[0;32m 95\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m agrees\u001b[38;5;241m.\u001b[39msum()\u001b[38;5;241m/\u001b[39m\u001b[38;5;28mlen\u001b[39m(agrees)\n", + "File \u001b[1;32mE:\\WORK\\DO\\2022\\palin\\python\\palin\\internal_noise\\double_pass.py:90\u001b[0m, in \u001b[0;36mDoublePass.compute_prob_agreement..same_answer\u001b[1;34m(group, trial_id, response_id)\u001b[0m\n\u001b[0;32m 89\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21msame_answer\u001b[39m(group, trial_id, response_id): \n\u001b[1;32m---> 90\u001b[0m d \u001b[38;5;241m=\u001b[39m \u001b[43mgroup\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgroupby\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrial_id\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43magg\u001b[49m\u001b[43m(\u001b[49m\u001b[43m{\u001b[49m\u001b[43mresponse_id\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mlambda\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mgroup\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mtuple\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mgroup\u001b[49m\u001b[43m)\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mreset_index()\n\u001b[0;32m 91\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m d\u001b[38;5;241m.\u001b[39mresponse\u001b[38;5;241m.\u001b[39mnunique()\u001b[38;5;241m==\u001b[39m\u001b[38;5;241m1\u001b[39m\n", + "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\core\\groupby\\generic.py:895\u001b[0m, in \u001b[0;36mDataFrameGroupBy.aggregate\u001b[1;34m(self, func, engine, engine_kwargs, *args, **kwargs)\u001b[0m\n\u001b[0;32m 892\u001b[0m func \u001b[38;5;241m=\u001b[39m maybe_mangle_lambdas(func)\n\u001b[0;32m 894\u001b[0m op \u001b[38;5;241m=\u001b[39m GroupByApply(\u001b[38;5;28mself\u001b[39m, func, args, kwargs)\n\u001b[1;32m--> 895\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mop\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43magg\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 896\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_dict_like(func) \u001b[38;5;129;01mand\u001b[39;00m result \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 897\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n", + "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\core\\apply.py:172\u001b[0m, in \u001b[0;36mApply.agg\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 169\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mapply_str()\n\u001b[0;32m 171\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_dict_like(arg):\n\u001b[1;32m--> 172\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43magg_dict_like\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 173\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m is_list_like(arg):\n\u001b[0;32m 174\u001b[0m \u001b[38;5;66;03m# we require a list, but not a 'str'\u001b[39;00m\n\u001b[0;32m 175\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39magg_list_like()\n", + "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\core\\apply.py:504\u001b[0m, in \u001b[0;36mApply.agg_dict_like\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 501\u001b[0m results \u001b[38;5;241m=\u001b[39m {key: colg\u001b[38;5;241m.\u001b[39magg(how) \u001b[38;5;28;01mfor\u001b[39;00m key, how \u001b[38;5;129;01min\u001b[39;00m arg\u001b[38;5;241m.\u001b[39mitems()}\n\u001b[0;32m 502\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 503\u001b[0m \u001b[38;5;66;03m# key used for column selection and output\u001b[39;00m\n\u001b[1;32m--> 504\u001b[0m results \u001b[38;5;241m=\u001b[39m {\n\u001b[0;32m 505\u001b[0m key: obj\u001b[38;5;241m.\u001b[39m_gotitem(key, ndim\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\u001b[38;5;241m.\u001b[39magg(how) \u001b[38;5;28;01mfor\u001b[39;00m key, how \u001b[38;5;129;01min\u001b[39;00m arg\u001b[38;5;241m.\u001b[39mitems()\n\u001b[0;32m 506\u001b[0m }\n\u001b[0;32m 508\u001b[0m \u001b[38;5;66;03m# set the final keys\u001b[39;00m\n\u001b[0;32m 509\u001b[0m keys \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(arg\u001b[38;5;241m.\u001b[39mkeys())\n", + "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\core\\apply.py:505\u001b[0m, in \u001b[0;36m\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m 501\u001b[0m results \u001b[38;5;241m=\u001b[39m {key: colg\u001b[38;5;241m.\u001b[39magg(how) \u001b[38;5;28;01mfor\u001b[39;00m key, how \u001b[38;5;129;01min\u001b[39;00m arg\u001b[38;5;241m.\u001b[39mitems()}\n\u001b[0;32m 502\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 503\u001b[0m \u001b[38;5;66;03m# key used for column selection and output\u001b[39;00m\n\u001b[0;32m 504\u001b[0m results \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m--> 505\u001b[0m key: \u001b[43mobj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_gotitem\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mndim\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43magg\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhow\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mfor\u001b[39;00m key, how \u001b[38;5;129;01min\u001b[39;00m arg\u001b[38;5;241m.\u001b[39mitems()\n\u001b[0;32m 506\u001b[0m }\n\u001b[0;32m 508\u001b[0m \u001b[38;5;66;03m# set the final keys\u001b[39;00m\n\u001b[0;32m 509\u001b[0m keys \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(arg\u001b[38;5;241m.\u001b[39mkeys())\n", + "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\core\\groupby\\generic.py:297\u001b[0m, in \u001b[0;36mSeriesGroupBy.aggregate\u001b[1;34m(self, func, engine, engine_kwargs, *args, **kwargs)\u001b[0m\n\u001b[0;32m 294\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_python_agg_general(func, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 296\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 297\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_python_agg_general\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 298\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m:\n\u001b[0;32m 299\u001b[0m \u001b[38;5;66;03m# TODO: KeyError is raised in _python_agg_general,\u001b[39;00m\n\u001b[0;32m 300\u001b[0m \u001b[38;5;66;03m# see test_groupby.test_basic\u001b[39;00m\n\u001b[0;32m 301\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_aggregate_named(func, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", + "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\core\\groupby\\groupby.py:1673\u001b[0m, in \u001b[0;36mGroupBy._python_agg_general\u001b[1;34m(self, func, raise_on_typeerror, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1670\u001b[0m \u001b[38;5;66;03m# iterate through \"columns\" ex exclusions to populate output dict\u001b[39;00m\n\u001b[0;32m 1671\u001b[0m output: \u001b[38;5;28mdict\u001b[39m[base\u001b[38;5;241m.\u001b[39mOutputKey, ArrayLike] \u001b[38;5;241m=\u001b[39m {}\n\u001b[1;32m-> 1673\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mngroups\u001b[49m \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m 1674\u001b[0m \u001b[38;5;66;03m# agg_series below assumes ngroups > 0\u001b[39;00m\n\u001b[0;32m 1675\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_python_apply_general(f, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_selected_obj, is_agg\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m 1677\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m idx, obj \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_iterate_slices()):\n", + "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\core\\groupby\\groupby.py:677\u001b[0m, in \u001b[0;36mBaseGroupBy.ngroups\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 674\u001b[0m \u001b[38;5;129m@final\u001b[39m\n\u001b[0;32m 675\u001b[0m \u001b[38;5;129m@property\u001b[39m\n\u001b[0;32m 676\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mngroups\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mint\u001b[39m:\n\u001b[1;32m--> 677\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgrouper\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mngroups\u001b[49m\n", + "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\_libs\\properties.pyx:36\u001b[0m, in \u001b[0;36mpandas._libs.properties.CachedProperty.__get__\u001b[1;34m()\u001b[0m\n", + "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\core\\groupby\\ops.py:982\u001b[0m, in \u001b[0;36mBaseGrouper.ngroups\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 979\u001b[0m \u001b[38;5;129m@final\u001b[39m\n\u001b[0;32m 980\u001b[0m \u001b[38;5;129m@cache_readonly\u001b[39m\n\u001b[0;32m 981\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mngroups\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mint\u001b[39m:\n\u001b[1;32m--> 982\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mresult_index\u001b[49m)\n", + "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\_libs\\properties.pyx:36\u001b[0m, in \u001b[0;36mpandas._libs.properties.CachedProperty.__get__\u001b[1;34m()\u001b[0m\n", + "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\core\\groupby\\ops.py:993\u001b[0m, in \u001b[0;36mBaseGrouper.result_index\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 990\u001b[0m \u001b[38;5;129m@cache_readonly\u001b[39m\n\u001b[0;32m 991\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mresult_index\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Index:\n\u001b[0;32m 992\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgroupings) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m--> 993\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgroupings\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mresult_index\u001b[49m\u001b[38;5;241m.\u001b[39mrename(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnames[\u001b[38;5;241m0\u001b[39m])\n\u001b[0;32m 995\u001b[0m codes \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mreconstructed_codes\n\u001b[0;32m 996\u001b[0m levels \u001b[38;5;241m=\u001b[39m [ping\u001b[38;5;241m.\u001b[39mresult_index \u001b[38;5;28;01mfor\u001b[39;00m ping \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgroupings]\n", + "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\_libs\\properties.pyx:36\u001b[0m, in \u001b[0;36mpandas._libs.properties.CachedProperty.__get__\u001b[1;34m()\u001b[0m\n", + "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\core\\groupby\\grouper.py:647\u001b[0m, in \u001b[0;36mGrouping.result_index\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 645\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(group_idx, CategoricalIndex)\n\u001b[0;32m 646\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m recode_from_groupby(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_all_grouper, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sort, group_idx)\n\u001b[1;32m--> 647\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgroup_index\u001b[49m\n", + "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\_libs\\properties.pyx:36\u001b[0m, in \u001b[0;36mpandas._libs.properties.CachedProperty.__get__\u001b[1;34m()\u001b[0m\n", + "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\core\\groupby\\grouper.py:655\u001b[0m, in \u001b[0;36mGrouping.group_index\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 651\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_group_index \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 652\u001b[0m \u001b[38;5;66;03m# _group_index is set in __init__ for MultiIndex cases\u001b[39;00m\n\u001b[0;32m 653\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_group_index\n\u001b[1;32m--> 655\u001b[0m uniques \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_codes_and_uniques\u001b[49m[\u001b[38;5;241m1\u001b[39m]\n\u001b[0;32m 656\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m Index\u001b[38;5;241m.\u001b[39m_with_infer(uniques, name\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mname)\n", + "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\_libs\\properties.pyx:36\u001b[0m, in \u001b[0;36mpandas._libs.properties.CachedProperty.__get__\u001b[1;34m()\u001b[0m\n", + "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\core\\groupby\\grouper.py:692\u001b[0m, in \u001b[0;36mGrouping._codes_and_uniques\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 685\u001b[0m uniques \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m 686\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgrouping_vector\u001b[38;5;241m.\u001b[39mresult_index\u001b[38;5;241m.\u001b[39m_values \u001b[38;5;66;03m# type: ignore[assignment]\u001b[39;00m\n\u001b[0;32m 687\u001b[0m )\n\u001b[0;32m 688\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 689\u001b[0m \u001b[38;5;66;03m# GH35667, replace dropna=False with use_na_sentinel=False\u001b[39;00m\n\u001b[0;32m 690\u001b[0m \u001b[38;5;66;03m# error: Incompatible types in assignment (expression has type \"Union[\u001b[39;00m\n\u001b[0;32m 691\u001b[0m \u001b[38;5;66;03m# ndarray[Any, Any], Index]\", variable has type \"Categorical\")\u001b[39;00m\n\u001b[1;32m--> 692\u001b[0m codes, uniques \u001b[38;5;241m=\u001b[39m \u001b[43malgorithms\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfactorize\u001b[49m\u001b[43m(\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# type: ignore[assignment]\u001b[39;49;00m\n\u001b[0;32m 693\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgrouping_vector\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msort\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_sort\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43muse_na_sentinel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_dropna\u001b[49m\n\u001b[0;32m 694\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 695\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m codes, uniques\n", + "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\core\\algorithms.py:832\u001b[0m, in \u001b[0;36mfactorize\u001b[1;34m(values, sort, na_sentinel, use_na_sentinel, size_hint)\u001b[0m\n\u001b[0;32m 829\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m na_sentinel \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 830\u001b[0m \u001b[38;5;66;03m# TODO: Can remove when na_sentinel=na_sentinel as in TODO above\u001b[39;00m\n\u001b[0;32m 831\u001b[0m na_sentinel \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m\n\u001b[1;32m--> 832\u001b[0m uniques, codes \u001b[38;5;241m=\u001b[39m \u001b[43msafe_sort\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 833\u001b[0m \u001b[43m \u001b[49m\u001b[43muniques\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcodes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mna_sentinel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mna_sentinel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43massume_unique\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mverify\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\n\u001b[0;32m 834\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 836\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m dropna \u001b[38;5;129;01mand\u001b[39;00m sort:\n\u001b[0;32m 837\u001b[0m \u001b[38;5;66;03m# TODO: Can remove entire block when na_sentinel=na_sentinel as in TODO above\u001b[39;00m\n\u001b[0;32m 838\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m na_sentinel \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", + "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\core\\algorithms.py:1875\u001b[0m, in \u001b[0;36msafe_sort\u001b[1;34m(values, codes, na_sentinel, assume_unique, verify)\u001b[0m\n\u001b[0;32m 1873\u001b[0m ordered \u001b[38;5;241m=\u001b[39m original_values\u001b[38;5;241m.\u001b[39mtake(sorter)\n\u001b[0;32m 1874\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1875\u001b[0m ordered \u001b[38;5;241m=\u001b[39m \u001b[43mvalues\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtake\u001b[49m\u001b[43m(\u001b[49m\u001b[43msorter\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1876\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[0;32m 1877\u001b[0m \u001b[38;5;66;03m# Previous sorters failed or were not applicable, try `_sort_mixed`\u001b[39;00m\n\u001b[0;32m 1878\u001b[0m \u001b[38;5;66;03m# which would work, but which fails for special case of 1d arrays\u001b[39;00m\n\u001b[0;32m 1879\u001b[0m \u001b[38;5;66;03m# with tuples.\u001b[39;00m\n\u001b[0;32m 1880\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m values\u001b[38;5;241m.\u001b[39msize \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(values[\u001b[38;5;241m0\u001b[39m], \u001b[38;5;28mtuple\u001b[39m):\n", + "\u001b[1;31mKeyboardInterrupt\u001b[0m: " + ] } ], "source": [ @@ -208,13 +248,12 @@ " 'n_features': [1],\n", " 'external_noise_std': [100]}\n", "analyser_params = {'internal_noise_extractor':[DoublePass], \n", - " 'model_file': ['model_11_04_2024.csv'], \n", - " 'rebuild_model': [False],\n", + " 'model_file': ['model_large.csv'], \n", + " 'rebuild_model': [False],\n", " 'internal_noise_range':[np.arange(0,5.1,0.1)],\n", - " 'criteria_range':[np.arange(-5,5,0.1)]\n", - " }\n", - "\n", - "\n", + " 'criteria_range':[np.arange(-5,5,0.1)],\n", + " 'n_runs':[2]}\n", + " \n", "sim = Sim(DoublePassExperiment, experiment_params, \n", " LinearObserver, observer_params, \n", " InternalNoiseValue, analyser_params)\n", @@ -224,147 +263,2238 @@ }, { "cell_type": "code", - "execution_count": 177, - "id": "0264e72a", + "execution_count": 216, + "id": "d36ad464", "metadata": { "ExecuteTime": { - "end_time": "2024-04-11T04:11:28.822698Z", - "start_time": "2024-04-11T04:11:28.763833Z" + "end_time": "2024-04-11T11:28:06.869895Z", + "start_time": "2024-04-11T11:28:06.817000Z" } }, "outputs": [ { "data": { - "text/html": [ - "
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n_trialsn_repeatedtrial_typen_featuresexternal_noise_stdkernelinternal_noise_stdcriteriainternal_noise_extractormodel_filerunmetric
010001000<class 'palin.simulation.trial.Int2Trial'>1100[1]0-1<class 'palin.internal_noise.double_pass.Doubl...model.csv00.0
110001000<class 'palin.simulation.trial.Int2Trial'>1100[1]01<class 'palin.internal_noise.double_pass.Doubl...model.csv00.0
210001000<class 'palin.simulation.trial.Int2Trial'>1100[1]1-1<class 'palin.internal_noise.double_pass.Doubl...model.csv01.0
310001000<class 'palin.simulation.trial.Int2Trial'>1100[1]11<class 'palin.internal_noise.double_pass.Doubl...model.csv00.7
\n", - "
" - ], "text/plain": [ - " n_trials n_repeated trial_type \\\n", - "0 1000 1000 \n", - "1 1000 1000 \n", - "2 1000 1000 \n", - "3 1000 1000 \n", - "\n", - " n_features external_noise_std kernel internal_noise_std criteria \\\n", - "0 1 100 [1] 0 -1 \n", - "1 1 100 [1] 0 1 \n", - "2 1 100 [1] 1 -1 \n", - "3 1 100 [1] 1 1 \n", - "\n", - " internal_noise_extractor model_file run metric \n", - "0 , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.0, 'criteria': -5.0}\n", + "..........;\n", + "1 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.0, 'criteria': -4.5}\n", + "..........;\n", + "2 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.0, 'criteria': -4.0}\n", + "..........;\n", + "3 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.0, 'criteria': -3.5}\n", + "..........;\n", + "4 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.0, 'criteria': -3.0}\n", + "..........;\n", + "5 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.0, 'criteria': -2.5}\n", + "..........;\n", + "6 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.0, 'criteria': -2.0}\n", + "..........;\n", + "7 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.0, 'criteria': -1.5}\n", + "..........;\n", + "8 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.0, 'criteria': -1.0}\n", + "..........;\n", + "9 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.0, 'criteria': -0.5}\n", + "..........;\n", + "10 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.0, 'criteria': 0.0}\n", + "..........;\n", + "11 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.0, 'criteria': 0.5}\n", + "..........;\n", + "12 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.0, 'criteria': 1.0}\n", + "..........;\n", + "13 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.0, 'criteria': 1.5}\n", + "..........;\n", + "14 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.0, 'criteria': 2.0}\n", + "..........;\n", + "15 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.0, 'criteria': 2.5}\n", + "..........;\n", + "16 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.0, 'criteria': 3.0}\n", + "..........;\n", + "17 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.0, 'criteria': 3.5}\n", + "..........;\n", + "18 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.0, 'criteria': 4.0}\n", + "..........;\n", + "19 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.0, 'criteria': 4.5}\n", + "..........;\n", + "20 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.1, 'criteria': -5.0}\n", + "..........;\n", + "21 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.1, 'criteria': -4.5}\n", + "..........;\n", + "22 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.1, 'criteria': -4.0}\n", + "..........;\n", + "23 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.1, 'criteria': -3.5}\n", + "..........;\n", + "24 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.1, 'criteria': -3.0}\n", + "..........;\n", + "25 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.1, 'criteria': -2.5}\n", + "..........;\n", + "26 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.1, 'criteria': -2.0}\n", + "..........;\n", + "27 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.1, 'criteria': -1.5}\n", + "..........;\n", + "28 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.1, 'criteria': -1.0}\n", + "..........;\n", + "29 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.1, 'criteria': -0.5}\n", + "..........;\n", + "30 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.1, 'criteria': 0.0}\n", + "..........;\n", + "31 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.1, 'criteria': 0.5}\n", + "..........;\n", + "32 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.1, 'criteria': 1.0}\n", + "..........;\n", + "33 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.1, 'criteria': 1.5}\n", + "..........;\n", + "34 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.1, 'criteria': 2.0}\n", + "..........;\n", + "35 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.1, 'criteria': 2.5}\n", + "..........;\n", + "36 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.1, 'criteria': 3.0}\n", + "..........;\n", + "37 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.1, 'criteria': 3.5}\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "..........;\n", + "38 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.1, 'criteria': 4.0}\n", + "..........;\n", + "39 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.1, 'criteria': 4.5}\n", + "..........;\n", + "40 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.2, 'criteria': -5.0}\n", + "..........;\n", + "41 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.2, 'criteria': -4.5}\n", + "..........;\n", + "42 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.2, 'criteria': -4.0}\n", + "..........;\n", + "43 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.2, 'criteria': -3.5}\n", + "..........;\n", + "44 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.2, 'criteria': -3.0}\n", + "..........;\n", + "45 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.2, 'criteria': -2.5}\n", + "..........;\n", + "46 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.2, 'criteria': -2.0}\n", + "..........;\n", + "47 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.2, 'criteria': -1.5}\n", + "..........;\n", + "48 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.2, 'criteria': -1.0}\n", + "..........;\n", + "49 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.2, 'criteria': -0.5}\n", + "..........;\n", + "50 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.2, 'criteria': 0.0}\n", + "..........;\n", + "51 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.2, 'criteria': 0.5}\n", + "..........;\n", + "52 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.2, 'criteria': 1.0}\n", + "..........;\n", + "53 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.2, 'criteria': 1.5}\n", + "..........;\n", + "54 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.2, 'criteria': 2.0}\n", + "..........;\n", + "55 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.2, 'criteria': 2.5}\n", + "..........;\n", + "56 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.2, 'criteria': 3.0}\n", + "..........;\n", + "57 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.2, 'criteria': 3.5}\n", + "..........;\n", + "58 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.2, 'criteria': 4.0}\n", + "..........;\n", + "59 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.2, 'criteria': 4.5}\n", + "..........;\n", + "60 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.30000000000000004, 'criteria': -5.0}\n", + "..........;\n", + "61 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.30000000000000004, 'criteria': -4.5}\n", + "..........;\n", + "62 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.30000000000000004, 'criteria': -4.0}\n", + "..........;\n", + "63 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.30000000000000004, 'criteria': -3.5}\n", + "..........;\n", + "64 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.30000000000000004, 'criteria': -3.0}\n", + "..........;\n", + "65 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.30000000000000004, 'criteria': -2.5}\n", + "..........;\n", + "66 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.30000000000000004, 'criteria': -2.0}\n", + "..........;\n", + "67 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.30000000000000004, 'criteria': -1.5}\n", + "..........;\n", + "68 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.30000000000000004, 'criteria': -1.0}\n", + "..........;\n", + "69 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.30000000000000004, 'criteria': -0.5}\n", + "..........;\n", + "70 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.30000000000000004, 'criteria': 0.0}\n", + "..........;\n", + "71 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.30000000000000004, 'criteria': 0.5}\n", + "..........;\n", + "72 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.30000000000000004, 'criteria': 1.0}\n", + "..........;\n", + "73 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.30000000000000004, 'criteria': 1.5}\n", + "..........;\n", + "74 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.30000000000000004, 'criteria': 2.0}\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "..........;\n", + "75 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.30000000000000004, 'criteria': 2.5}\n", + "..........;\n", + "76 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.30000000000000004, 'criteria': 3.0}\n", + "..........;\n", + "77 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.30000000000000004, 'criteria': 3.5}\n", + "..........;\n", + "78 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.30000000000000004, 'criteria': 4.0}\n", + "..........;\n", + "79 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.30000000000000004, 'criteria': 4.5}\n", + "..........;\n", + "80 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.4, 'criteria': -5.0}\n", + "..........;\n", + "81 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.4, 'criteria': -4.5}\n", + "..........;\n", + "82 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.4, 'criteria': -4.0}\n", + "..........;\n", + "83 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.4, 'criteria': -3.5}\n", + "..........;\n", + "84 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.4, 'criteria': -3.0}\n", + "..........;\n", + "85 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.4, 'criteria': -2.5}\n", + "..........;\n", + "86 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.4, 'criteria': -2.0}\n", + "..........;\n", + "87 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.4, 'criteria': -1.5}\n", + "..........;\n", + "88 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.4, 'criteria': -1.0}\n", + "..........;\n", + "89 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.4, 'criteria': -0.5}\n", + "..........;\n", + "90 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.4, 'criteria': 0.0}\n", + "..........;\n", + "91 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.4, 'criteria': 0.5}\n", + "..........;\n", + "92 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.4, 'criteria': 1.0}\n", + "..........;\n", + "93 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.4, 'criteria': 1.5}\n", + "..........;\n", + "94 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.4, 'criteria': 2.0}\n", + "..........;\n", + "95 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.4, 'criteria': 2.5}\n", + "..........;\n", + "96 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.4, 'criteria': 3.0}\n", + "..........;\n", + "97 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.4, 'criteria': 3.5}\n", + "..........;\n", + "98 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.4, 'criteria': 4.0}\n", + "..........;\n", + "99 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.4, 'criteria': 4.5}\n", + "..........;\n", + "100 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.5, 'criteria': -5.0}\n", + "..........;\n", + "101 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.5, 'criteria': -4.5}\n", + "..........;\n", + "102 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.5, 'criteria': -4.0}\n", + "..........;\n", + "103 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.5, 'criteria': -3.5}\n", + "..........;\n", + "104 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.5, 'criteria': -3.0}\n", + "..........;\n", + "105 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.5, 'criteria': -2.5}\n", + "..........;\n", + "106 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.5, 'criteria': -2.0}\n", + "..........;\n", + "107 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.5, 'criteria': -1.5}\n", + "..........;\n", + "108 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.5, 'criteria': -1.0}\n", + "..........;\n", + "109 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.5, 'criteria': -0.5}\n", + "..........;\n", + "110 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.5, 'criteria': 0.0}\n", + "..........;\n", + "111 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.5, 'criteria': 0.5}\n", + "..........;\n", + "112 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.5, 'criteria': 1.0}\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "..........;\n", + "113 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.5, 'criteria': 1.5}\n", + "..........;\n", + "114 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.5, 'criteria': 2.0}\n", + "..........;\n", + "115 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.5, 'criteria': 2.5}\n", + "..........;\n", + "116 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.5, 'criteria': 3.0}\n", + "..........;\n", + "117 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.5, 'criteria': 3.5}\n", + "..........;\n", + "118 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.5, 'criteria': 4.0}\n", + "..........;\n", + "119 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.5, 'criteria': 4.5}\n", + "..........;\n", + "120 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.6000000000000001, 'criteria': -5.0}\n", + "..........;\n", + "121 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.6000000000000001, 'criteria': -4.5}\n", + "..........;\n", + "122 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.6000000000000001, 'criteria': -4.0}\n", + "..........;\n", + "123 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.6000000000000001, 'criteria': -3.5}\n", + "..........;\n", + "124 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.6000000000000001, 'criteria': -3.0}\n", + "..........;\n", + "125 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.6000000000000001, 'criteria': -2.5}\n", + "..........;\n", + "126 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.6000000000000001, 'criteria': -2.0}\n", + "..........;\n", + "127 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.6000000000000001, 'criteria': -1.5}\n", + "..........;\n", + "128 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.6000000000000001, 'criteria': -1.0}\n", + "..........;\n", + "129 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.6000000000000001, 'criteria': -0.5}\n", + "..........;\n", + "130 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.6000000000000001, 'criteria': 0.0}\n", + "..........;\n", + "131 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.6000000000000001, 'criteria': 0.5}\n", + "..........;\n", + "132 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.6000000000000001, 'criteria': 1.0}\n", + "..........;\n", + "133 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.6000000000000001, 'criteria': 1.5}\n", + "..........;\n", + "134 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.6000000000000001, 'criteria': 2.0}\n", + "..........;\n", + "135 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.6000000000000001, 'criteria': 2.5}\n", + "..........;\n", + "136 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.6000000000000001, 'criteria': 3.0}\n", + "..........;\n", + "137 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.6000000000000001, 'criteria': 3.5}\n", + "..........;\n", + "138 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.6000000000000001, 'criteria': 4.0}\n", + "..........;\n", + "139 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.6000000000000001, 'criteria': 4.5}\n", + "..........;\n", + "140 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.7000000000000001, 'criteria': -5.0}\n", + "..........;\n", + "141 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.7000000000000001, 'criteria': -4.5}\n", + "..........;\n", + "142 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.7000000000000001, 'criteria': -4.0}\n", + "..........;\n", + "143 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.7000000000000001, 'criteria': -3.5}\n", + "..........;\n", + "144 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.7000000000000001, 'criteria': -3.0}\n", + "..........;\n", + "145 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.7000000000000001, 'criteria': -2.5}\n", + "..........;\n", + "146 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.7000000000000001, 'criteria': -2.0}\n", + "..........;\n", + "147 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.7000000000000001, 'criteria': -1.5}\n", + "..........;\n", + "148 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.7000000000000001, 'criteria': -1.0}\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "..........;\n", + "149 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.7000000000000001, 'criteria': -0.5}\n", + "..........;\n", + "150 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.7000000000000001, 'criteria': 0.0}\n", + "..........;\n", + "151 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.7000000000000001, 'criteria': 0.5}\n", + "..........;\n", + "152 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.7000000000000001, 'criteria': 1.0}\n", + "..........;\n", + "153 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.7000000000000001, 'criteria': 1.5}\n", + "..........;\n", + "154 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.7000000000000001, 'criteria': 2.0}\n", + "..........;\n", + "155 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.7000000000000001, 'criteria': 2.5}\n", + "..........;\n", + "156 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.7000000000000001, 'criteria': 3.0}\n", + "..........;\n", + "157 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.7000000000000001, 'criteria': 3.5}\n", + "..........;\n", + "158 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.7000000000000001, 'criteria': 4.0}\n", + "..........;\n", + "159 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.7000000000000001, 'criteria': 4.5}\n", + "..........;\n", + "160 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.8, 'criteria': -5.0}\n", + "..........;\n", + "161 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.8, 'criteria': -4.5}\n", + "..........;\n", + "162 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.8, 'criteria': -4.0}\n", + "..........;\n", + "163 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.8, 'criteria': -3.5}\n", + "..........;\n", + "164 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.8, 'criteria': -3.0}\n", + "..........;\n", + "165 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.8, 'criteria': -2.5}\n", + "..........;\n", + "166 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.8, 'criteria': -2.0}\n", + "..........;\n", + "167 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.8, 'criteria': -1.5}\n", + "..........;\n", + "168 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.8, 'criteria': -1.0}\n", + "..........;\n", + "169 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.8, 'criteria': -0.5}\n", + "..........;\n", + "170 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.8, 'criteria': 0.0}\n", + "..........;\n", + "171 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.8, 'criteria': 0.5}\n", + "..........;\n", + "172 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.8, 'criteria': 1.0}\n", + "..........;\n", + "173 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.8, 'criteria': 1.5}\n", + "..........;\n", + "174 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.8, 'criteria': 2.0}\n", + "..........;\n", + "175 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.8, 'criteria': 2.5}\n", + "..........;\n", + "176 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.8, 'criteria': 3.0}\n", + "..........;\n", + "177 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.8, 'criteria': 3.5}\n", + "..........;\n", + "178 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.8, 'criteria': 4.0}\n", + "..........;\n", + "179 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.8, 'criteria': 4.5}\n", + "..........;\n", + "180 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.9, 'criteria': -5.0}\n", + "..........;\n", + "181 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.9, 'criteria': -4.5}\n", + "..........;\n", + "182 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.9, 'criteria': -4.0}\n", + "..........;\n", + "183 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.9, 'criteria': -3.5}\n", + "..........;\n", + "184 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.9, 'criteria': -3.0}\n", + "..........;\n", + "185 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.9, 'criteria': -2.5}\n", + "..........;\n", + "186 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.9, 'criteria': -2.0}\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "..........;\n", + "187 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.9, 'criteria': -1.5}\n", + "..........;\n", + "188 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.9, 'criteria': -1.0}\n", + "..........;\n", + "189 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.9, 'criteria': -0.5}\n", + "..........;\n", + "190 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.9, 'criteria': 0.0}\n", + "..........;\n", + "191 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.9, 'criteria': 0.5}\n", + "..........;\n", + "192 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.9, 'criteria': 1.0}\n", + "..........;\n", + "193 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.9, 'criteria': 1.5}\n", + "..........;\n", + "194 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.9, 'criteria': 2.0}\n", + "..........;\n", + "195 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.9, 'criteria': 2.5}\n", + "..........;\n", + "196 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.9, 'criteria': 3.0}\n", + "..........;\n", + "197 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.9, 'criteria': 3.5}\n", + "..........;\n", + "198 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.9, 'criteria': 4.0}\n", + "..........;\n", + "199 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.9, 'criteria': 4.5}\n", + "..........;\n", + "200 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.0, 'criteria': -5.0}\n", + "..........;\n", + "201 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.0, 'criteria': -4.5}\n", + "..........;\n", + "202 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.0, 'criteria': -4.0}\n", + "..........;\n", + "203 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.0, 'criteria': -3.5}\n", + "..........;\n", + "204 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.0, 'criteria': -3.0}\n", + "..........;\n", + "205 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.0, 'criteria': -2.5}\n", + "..........;\n", + "206 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.0, 'criteria': -2.0}\n", + "..........;\n", + "207 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.0, 'criteria': -1.5}\n", + "..........;\n", + "208 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.0, 'criteria': -1.0}\n", + "..........;\n", + "209 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.0, 'criteria': -0.5}\n", + "..........;\n", + "210 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.0, 'criteria': 0.0}\n", + "..........;\n", + "211 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.0, 'criteria': 0.5}\n", + "..........;\n", + "212 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.0, 'criteria': 1.0}\n", + "..........;\n", + "213 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.0, 'criteria': 1.5}\n", + "..........;\n", + "214 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.0, 'criteria': 2.0}\n", + "..........;\n", + "215 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.0, 'criteria': 2.5}\n", + "..........;\n", + "216 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.0, 'criteria': 3.0}\n", + "..........;\n", + "217 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.0, 'criteria': 3.5}\n", + "..........;\n", + "218 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.0, 'criteria': 4.0}\n", + "..........;\n", + "219 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.0, 'criteria': 4.5}\n", + "..........;\n", + "220 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.1, 'criteria': -5.0}\n", + "..........;\n", + "221 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.1, 'criteria': -4.5}\n", + "..........;\n", + "222 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.1, 'criteria': -4.0}\n", + "..........;\n", + "223 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.1, 'criteria': -3.5}\n", + "..........;\n", + "224 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.1, 'criteria': -3.0}\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "..........;\n", + "225 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.1, 'criteria': -2.5}\n", + "..........;\n", + "226 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.1, 'criteria': -2.0}\n", + "..........;\n", + "227 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.1, 'criteria': -1.5}\n", + "..........;\n", + "228 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.1, 'criteria': -1.0}\n", + "..........;\n", + "229 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.1, 'criteria': -0.5}\n", + "..........;\n", + "230 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.1, 'criteria': 0.0}\n", + "..........;\n", + "231 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.1, 'criteria': 0.5}\n", + "..........;\n", + "232 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.1, 'criteria': 1.0}\n", + "..........;\n", + "233 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.1, 'criteria': 1.5}\n", + "..........;\n", + "234 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.1, 'criteria': 2.0}\n", + "..........;\n", + "235 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.1, 'criteria': 2.5}\n", + "..........;\n", + "236 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.1, 'criteria': 3.0}\n", + "..........;\n", + "237 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.1, 'criteria': 3.5}\n", + "..........;\n", + "238 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.1, 'criteria': 4.0}\n", + "..........;\n", + "239 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.1, 'criteria': 4.5}\n", + "..........;\n", + "240 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.2000000000000002, 'criteria': -5.0}\n", + "..........;\n", + "241 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.2000000000000002, 'criteria': -4.5}\n", + "..........;\n", + "242 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.2000000000000002, 'criteria': -4.0}\n", + "..........;\n", + "243 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.2000000000000002, 'criteria': -3.5}\n", + "..........;\n", + "244 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.2000000000000002, 'criteria': -3.0}\n", + "..........;\n", + "245 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.2000000000000002, 'criteria': -2.5}\n", + "..........;\n", + "246 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.2000000000000002, 'criteria': -2.0}\n", + "..........;\n", + "247 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.2000000000000002, 'criteria': -1.5}\n", + "..........;\n", + "248 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.2000000000000002, 'criteria': -1.0}\n", + "..........;\n", + "249 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.2000000000000002, 'criteria': -0.5}\n", + "..........;\n", + "250 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.2000000000000002, 'criteria': 0.0}\n", + "..........;\n", + "251 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.2000000000000002, 'criteria': 0.5}\n", + "..........;\n", + "252 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.2000000000000002, 'criteria': 1.0}\n", + "..........;\n", + "253 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.2000000000000002, 'criteria': 1.5}\n", + "..........;\n", + "254 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.2000000000000002, 'criteria': 2.0}\n", + "..........;\n", + "255 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.2000000000000002, 'criteria': 2.5}\n", + "..........;\n", + "256 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.2000000000000002, 'criteria': 3.0}\n", + "..........;\n", + "257 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.2000000000000002, 'criteria': 3.5}\n", + "..........;\n", + "258 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.2000000000000002, 'criteria': 4.0}\n", + "..........;\n", + "259 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.2000000000000002, 'criteria': 4.5}\n", + "..........;\n", + "260 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.3, 'criteria': -5.0}\n", + "..........;\n", + "261 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.3, 'criteria': -4.5}\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "..........;\n", + "262 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.3, 'criteria': -4.0}\n", + "..........;\n", + "263 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.3, 'criteria': -3.5}\n", + "..........;\n", + "264 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.3, 'criteria': -3.0}\n", + "..........;\n", + "265 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.3, 'criteria': -2.5}\n", + "..........;\n", + "266 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.3, 'criteria': -2.0}\n", + "..........;\n", + "267 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.3, 'criteria': -1.5}\n", + "..........;\n", + "268 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.3, 'criteria': -1.0}\n", + "..........;\n", + "269 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.3, 'criteria': -0.5}\n", + "..........;\n", + "270 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.3, 'criteria': 0.0}\n", + "..........;\n", + "271 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.3, 'criteria': 0.5}\n", + "..........;\n", + "272 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.3, 'criteria': 1.0}\n", + "..........;\n", + "273 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.3, 'criteria': 1.5}\n", + "..........;\n", + "274 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.3, 'criteria': 2.0}\n", + "..........;\n", + "275 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.3, 'criteria': 2.5}\n", + "..........;\n", + "276 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.3, 'criteria': 3.0}\n", + "..........;\n", + "277 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.3, 'criteria': 3.5}\n", + "..........;\n", + "278 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.3, 'criteria': 4.0}\n", + "..........;\n", + "279 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.3, 'criteria': 4.5}\n", + "..........;\n", + "280 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.4000000000000001, 'criteria': -5.0}\n", + "..........;\n", + "281 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.4000000000000001, 'criteria': -4.5}\n", + "..........;\n", + "282 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.4000000000000001, 'criteria': -4.0}\n", + "..........;\n", + "283 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.4000000000000001, 'criteria': -3.5}\n", + "..........;\n", + "284 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.4000000000000001, 'criteria': -3.0}\n", + "..........;\n", + "285 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.4000000000000001, 'criteria': -2.5}\n", + "..........;\n", + "286 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.4000000000000001, 'criteria': -2.0}\n", + "..........;\n", + "287 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.4000000000000001, 'criteria': -1.5}\n", + "..........;\n", + "288 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.4000000000000001, 'criteria': -1.0}\n", + "..........;\n", + "289 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.4000000000000001, 'criteria': -0.5}\n", + "..........;\n", + "290 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.4000000000000001, 'criteria': 0.0}\n", + "..........;\n", + "291 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.4000000000000001, 'criteria': 0.5}\n", + "..........;\n", + "292 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.4000000000000001, 'criteria': 1.0}\n", + "..........;\n", + "293 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.4000000000000001, 'criteria': 1.5}\n", + "..........;\n", + "294 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.4000000000000001, 'criteria': 2.0}\n", + "..........;\n", + "295 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.4000000000000001, 'criteria': 2.5}\n", + "..........;\n", + "296 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.4000000000000001, 'criteria': 3.0}\n", + "..........;\n", + "297 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.4000000000000001, 'criteria': 3.5}\n", + "..........;\n", + "298 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.4000000000000001, 'criteria': 4.0}\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "..........;\n", + "299 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.4000000000000001, 'criteria': 4.5}\n", + "..........;\n", + "300 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.5, 'criteria': -5.0}\n", + "..........;\n", + "301 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.5, 'criteria': -4.5}\n", + "..........;\n", + "302 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.5, 'criteria': -4.0}\n", + "..........;\n", + "303 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.5, 'criteria': -3.5}\n", + "..........;\n", + "304 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.5, 'criteria': -3.0}\n", + "..........;\n", + "305 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.5, 'criteria': -2.5}\n", + "..........;\n", + "306 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.5, 'criteria': -2.0}\n", + "..........;\n", + "307 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.5, 'criteria': -1.5}\n", + "..........;\n", + "308 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.5, 'criteria': -1.0}\n", + "..........;\n", + "309 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.5, 'criteria': -0.5}\n", + "..........;\n", + "310 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.5, 'criteria': 0.0}\n", + "..........;\n", + "311 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.5, 'criteria': 0.5}\n", + "..........;\n", + "312 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.5, 'criteria': 1.0}\n", + "..........;\n", + "313 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.5, 'criteria': 1.5}\n", + "..........;\n", + "314 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.5, 'criteria': 2.0}\n", + "..........;\n", + "315 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.5, 'criteria': 2.5}\n", + "..........;\n", + "316 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.5, 'criteria': 3.0}\n", + "..........;\n", + "317 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.5, 'criteria': 3.5}\n", + "..........;\n", + "318 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.5, 'criteria': 4.0}\n", + "..........;\n", + "319 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.5, 'criteria': 4.5}\n", + "..........;\n", + "320 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.6, 'criteria': -5.0}\n", + "..........;\n", + "321 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.6, 'criteria': -4.5}\n", + "..........;\n", + "322 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.6, 'criteria': -4.0}\n", + "..........;\n", + "323 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.6, 'criteria': -3.5}\n", + "..........;\n", + "324 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.6, 'criteria': -3.0}\n", + "..........;\n", + "325 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.6, 'criteria': -2.5}\n", + "..........;\n", + "326 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.6, 'criteria': -2.0}\n", + "..........;\n", + "327 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.6, 'criteria': -1.5}\n", + "..........;\n", + "328 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.6, 'criteria': -1.0}\n", + "..........;\n", + "329 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.6, 'criteria': -0.5}\n", + "..........;\n", + "330 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.6, 'criteria': 0.0}\n", + "..........;\n", + "331 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.6, 'criteria': 0.5}\n", + "..........;\n", + "332 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.6, 'criteria': 1.0}\n", + "..........;\n", + "333 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.6, 'criteria': 1.5}\n", + "..........;\n", + "334 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.6, 'criteria': 2.0}\n", + "..........;\n", + "335 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.6, 'criteria': 2.5}\n", + "..........;\n", + "336 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.6, 'criteria': 3.0}\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "..........;\n", + "337 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.6, 'criteria': 3.5}\n", + "..........;\n", + "338 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.6, 'criteria': 4.0}\n", + "..........;\n", + "339 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.6, 'criteria': 4.5}\n", + "..........;\n", + "340 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.7000000000000002, 'criteria': -5.0}\n", + "..........;\n", + "341 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.7000000000000002, 'criteria': -4.5}\n", + "..........;\n", + "342 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.7000000000000002, 'criteria': -4.0}\n", + "..........;\n", + "343 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.7000000000000002, 'criteria': -3.5}\n", + "..........;\n", + "344 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.7000000000000002, 'criteria': -3.0}\n", + "..........;\n", + "345 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.7000000000000002, 'criteria': -2.5}\n", + "..........;\n", + "346 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.7000000000000002, 'criteria': -2.0}\n", + "..........;\n", + "347 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.7000000000000002, 'criteria': -1.5}\n", + "..........;\n", + "348 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.7000000000000002, 'criteria': -1.0}\n", + "..........;\n", + "349 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.7000000000000002, 'criteria': -0.5}\n", + "..........;\n", + "350 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.7000000000000002, 'criteria': 0.0}\n", + "..........;\n", + "351 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.7000000000000002, 'criteria': 0.5}\n", + "..........;\n", + "352 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.7000000000000002, 'criteria': 1.0}\n", + "..........;\n", + "353 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.7000000000000002, 'criteria': 1.5}\n", + "..........;\n", + "354 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.7000000000000002, 'criteria': 2.0}\n", + "..........;\n", + "355 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.7000000000000002, 'criteria': 2.5}\n", + "..........;\n", + "356 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.7000000000000002, 'criteria': 3.0}\n", + "..........;\n", + "357 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.7000000000000002, 'criteria': 3.5}\n", + "..........;\n", + "358 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.7000000000000002, 'criteria': 4.0}\n", + "..........;\n", + "359 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.7000000000000002, 'criteria': 4.5}\n", + "..........;\n", + "360 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.8, 'criteria': -5.0}\n", + "..........;\n", + "361 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.8, 'criteria': -4.5}\n", + "..........;\n", + "362 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.8, 'criteria': -4.0}\n", + "..........;\n", + "363 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.8, 'criteria': -3.5}\n", + "..........;\n", + "364 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.8, 'criteria': -3.0}\n", + "..........;\n", + "365 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.8, 'criteria': -2.5}\n", + "..........;\n", + "366 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.8, 'criteria': -2.0}\n", + "..........;\n", + "367 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.8, 'criteria': -1.5}\n", + "..........;\n", + "368 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.8, 'criteria': -1.0}\n", + "..........;\n", + "369 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.8, 'criteria': -0.5}\n", + "..........;\n", + "370 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.8, 'criteria': 0.0}\n", + "..........;\n", + "371 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.8, 'criteria': 0.5}\n", + "..........;\n", + "372 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.8, 'criteria': 1.0}\n", + "..........;\n", + "373 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.8, 'criteria': 1.5}\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "..........;\n", + "374 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.8, 'criteria': 2.0}\n", + "..........;\n", + "375 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.8, 'criteria': 2.5}\n", + "..........;\n", + "376 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.8, 'criteria': 3.0}\n", + "..........;\n", + "377 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.8, 'criteria': 3.5}\n", + "..........;\n", + "378 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.8, 'criteria': 4.0}\n", + "..........;\n", + "379 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.8, 'criteria': 4.5}\n", + "..........;\n", + "380 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.9000000000000001, 'criteria': -5.0}\n", + "..........;\n", + "381 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.9000000000000001, 'criteria': -4.5}\n", + "..........;\n", + "382 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.9000000000000001, 'criteria': -4.0}\n", + "..........;\n", + "383 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.9000000000000001, 'criteria': -3.5}\n", + "..........;\n", + "384 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.9000000000000001, 'criteria': -3.0}\n", + "..........;\n", + "385 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.9000000000000001, 'criteria': -2.5}\n", + "..........;\n", + "386 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.9000000000000001, 'criteria': -2.0}\n", + "..........;\n", + "387 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.9000000000000001, 'criteria': -1.5}\n", + "..........;\n", + "388 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.9000000000000001, 'criteria': -1.0}\n", + "..........;\n", + "389 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.9000000000000001, 'criteria': -0.5}\n", + "..........;\n", + "390 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.9000000000000001, 'criteria': 0.0}\n", + "..........;\n", + "391 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.9000000000000001, 'criteria': 0.5}\n", + "..........;\n", + "392 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.9000000000000001, 'criteria': 1.0}\n", + "..........;\n", + "393 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.9000000000000001, 'criteria': 1.5}\n", + "..........;\n", + "394 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.9000000000000001, 'criteria': 2.0}\n", + "..........;\n", + "395 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.9000000000000001, 'criteria': 2.5}\n", + "..........;\n", + "396 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.9000000000000001, 'criteria': 3.0}\n", + "..........;\n", + "397 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.9000000000000001, 'criteria': 3.5}\n", + "..........;\n", + "398 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.9000000000000001, 'criteria': 4.0}\n", + "..........;\n", + "399 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.9000000000000001, 'criteria': 4.5}\n", + "..........;\n", + "400 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.0, 'criteria': -5.0}\n", + "..........;\n", + "401 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.0, 'criteria': -4.5}\n", + "..........;\n", + "402 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.0, 'criteria': -4.0}\n", + "..........;\n", + "403 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.0, 'criteria': -3.5}\n", + "..........;\n", + "404 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.0, 'criteria': -3.0}\n", + "..........;\n", + "405 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.0, 'criteria': -2.5}\n", + "..........;\n", + "406 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.0, 'criteria': -2.0}\n", + "..........;\n", + "407 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.0, 'criteria': -1.5}\n", + "..........;\n", + "408 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.0, 'criteria': -1.0}\n", + "..........;\n", + "409 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.0, 'criteria': -0.5}\n", + "..........;\n", + "410 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.0, 'criteria': 0.0}\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "..........;\n", + "411 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.0, 'criteria': 0.5}\n", + "..........;\n", + "412 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.0, 'criteria': 1.0}\n", + "..........;\n", + "413 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.0, 'criteria': 1.5}\n", + "..........;\n", + "414 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.0, 'criteria': 2.0}\n", + "..........;\n", + "415 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.0, 'criteria': 2.5}\n", + "..........;\n", + "416 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.0, 'criteria': 3.0}\n", + "..........;\n", + "417 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.0, 'criteria': 3.5}\n", + "..........;\n", + "418 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.0, 'criteria': 4.0}\n", + "..........;\n", + "419 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.0, 'criteria': 4.5}\n", + "..........;\n", + "420 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.1, 'criteria': -5.0}\n", + "..........;\n", + "421 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.1, 'criteria': -4.5}\n", + "..........;\n", + "422 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.1, 'criteria': -4.0}\n", + "..........;\n", + "423 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.1, 'criteria': -3.5}\n", + "..........;\n", + "424 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.1, 'criteria': -3.0}\n", + "..........;\n", + "425 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.1, 'criteria': -2.5}\n", + "..........;\n", + "426 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.1, 'criteria': -2.0}\n", + "..........;\n", + "427 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.1, 'criteria': -1.5}\n", + "..........;\n", + "428 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.1, 'criteria': -1.0}\n", + "..........;\n", + "429 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.1, 'criteria': -0.5}\n", + "..........;\n", + "430 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.1, 'criteria': 0.0}\n", + "..........;\n", + "431 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.1, 'criteria': 0.5}\n", + "..........;\n", + "432 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.1, 'criteria': 1.0}\n", + "..........;\n", + "433 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.1, 'criteria': 1.5}\n", + "..........;\n", + "434 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.1, 'criteria': 2.0}\n", + "..........;\n", + "435 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.1, 'criteria': 2.5}\n", + "..........;\n", + "436 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.1, 'criteria': 3.0}\n", + "..........;\n", + "437 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.1, 'criteria': 3.5}\n", + "..........;\n", + "438 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.1, 'criteria': 4.0}\n", + "..........;\n", + "439 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.1, 'criteria': 4.5}\n", + "..........;\n", + "440 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.2, 'criteria': -5.0}\n", + "..........;\n", + "441 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.2, 'criteria': -4.5}\n", + "..........;\n", + "442 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.2, 'criteria': -4.0}\n", + "..........;\n", + "443 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.2, 'criteria': -3.5}\n", + "..........;\n", + "444 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.2, 'criteria': -3.0}\n", + "..........;\n", + "445 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.2, 'criteria': -2.5}\n", + "..........;\n", + "446 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.2, 'criteria': -2.0}\n", + "..........;\n", + "447 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.2, 'criteria': -1.5}\n", + "..........;\n", + "448 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.2, 'criteria': -1.0}\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "..........;\n", + "449 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.2, 'criteria': -0.5}\n", + "..........;\n", + "450 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.2, 'criteria': 0.0}\n", + "..........;\n", + "451 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.2, 'criteria': 0.5}\n", + "..........;\n", + "452 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.2, 'criteria': 1.0}\n", + "..........;\n", + "453 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.2, 'criteria': 1.5}\n", + "..........;\n", + "454 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.2, 'criteria': 2.0}\n", + "..........;\n", + "455 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.2, 'criteria': 2.5}\n", + "..........;\n", + "456 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.2, 'criteria': 3.0}\n", + "..........;\n", + "457 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.2, 'criteria': 3.5}\n", + "..........;\n", + "458 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.2, 'criteria': 4.0}\n", + "..........;\n", + "459 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.2, 'criteria': 4.5}\n", + "..........;\n", + "460 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.3000000000000003, 'criteria': -5.0}\n", + "..........;\n", + "461 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.3000000000000003, 'criteria': -4.5}\n", + "..........;\n", + "462 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.3000000000000003, 'criteria': -4.0}\n", + "..........;\n", + "463 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.3000000000000003, 'criteria': -3.5}\n", + "..........;\n", + "464 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.3000000000000003, 'criteria': -3.0}\n", + "..........;\n", + "465 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.3000000000000003, 'criteria': -2.5}\n", + "..........;\n", + "466 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.3000000000000003, 'criteria': -2.0}\n", + "..........;\n", + "467 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.3000000000000003, 'criteria': -1.5}\n", + "..........;\n", + "468 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.3000000000000003, 'criteria': -1.0}\n", + "..........;\n", + "469 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.3000000000000003, 'criteria': -0.5}\n", + "..........;\n", + "470 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.3000000000000003, 'criteria': 0.0}\n", + "..........;\n", + "471 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.3000000000000003, 'criteria': 0.5}\n", + "..........;\n", + "472 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.3000000000000003, 'criteria': 1.0}\n", + "..........;\n", + "473 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.3000000000000003, 'criteria': 1.5}\n", + "..........;\n", + "474 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.3000000000000003, 'criteria': 2.0}\n", + "..........;\n", + "475 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.3000000000000003, 'criteria': 2.5}\n", + "..........;\n", + "476 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.3000000000000003, 'criteria': 3.0}\n", + "..........;\n", + "477 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.3000000000000003, 'criteria': 3.5}\n", + "..........;\n", + "478 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.3000000000000003, 'criteria': 4.0}\n", + "..........;\n", + "479 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.3000000000000003, 'criteria': 4.5}\n", + "..........;\n", + "480 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.4000000000000004, 'criteria': -5.0}\n", + "..........;\n", + "481 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.4000000000000004, 'criteria': -4.5}\n", + "..........;\n", + "482 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.4000000000000004, 'criteria': -4.0}\n", + "..........;\n", + "483 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.4000000000000004, 'criteria': -3.5}\n", + "..........;\n", + "484 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.4000000000000004, 'criteria': -3.0}\n", + "..........;\n", + "485 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.4000000000000004, 'criteria': -2.5}\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "..........;\n", + "486 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.4000000000000004, 'criteria': -2.0}\n", + "..........;\n", + "487 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.4000000000000004, 'criteria': -1.5}\n", + "..........;\n", + "488 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.4000000000000004, 'criteria': -1.0}\n", + "..........;\n", + "489 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.4000000000000004, 'criteria': -0.5}\n", + "..........;\n", + "490 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.4000000000000004, 'criteria': 0.0}\n", + "..........;\n", + "491 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.4000000000000004, 'criteria': 0.5}\n", + "..........;\n", + "492 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.4000000000000004, 'criteria': 1.0}\n", + "..........;\n", + "493 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.4000000000000004, 'criteria': 1.5}\n", + "..........;\n", + "494 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.4000000000000004, 'criteria': 2.0}\n", + "..........;\n", + "495 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.4000000000000004, 'criteria': 2.5}\n", + "..........;\n", + "496 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.4000000000000004, 'criteria': 3.0}\n", + "..........;\n", + "497 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.4000000000000004, 'criteria': 3.5}\n", + "..........;\n", + "498 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.4000000000000004, 'criteria': 4.0}\n", + "..........;\n", + "499 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.4000000000000004, 'criteria': 4.5}\n", + "..........;\n", + "500 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.5, 'criteria': -5.0}\n", + "..........;\n", + "501 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.5, 'criteria': -4.5}\n", + "..........;\n", + "502 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.5, 'criteria': -4.0}\n", + "..........;\n", + "503 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.5, 'criteria': -3.5}\n", + "..........;\n", + "504 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.5, 'criteria': -3.0}\n", + "..........;\n", + "505 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.5, 'criteria': -2.5}\n", + "..........;\n", + "506 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.5, 'criteria': -2.0}\n", + "..........;\n", + "507 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.5, 'criteria': -1.5}\n", + "..........;\n", + "508 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.5, 'criteria': -1.0}\n", + "..........;\n", + "509 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.5, 'criteria': -0.5}\n", + "..........;\n", + "510 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.5, 'criteria': 0.0}\n", + "..........;\n", + "511 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.5, 'criteria': 0.5}\n", + "..........;\n", + "512 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.5, 'criteria': 1.0}\n", + "..........;\n", + "513 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.5, 'criteria': 1.5}\n", + "..........;\n", + "514 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.5, 'criteria': 2.0}\n", + "..........;\n", + "515 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.5, 'criteria': 2.5}\n", + "..........;\n", + "516 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.5, 'criteria': 3.0}\n", + "..........;\n", + "517 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.5, 'criteria': 3.5}\n", + "..........;\n", + "518 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.5, 'criteria': 4.0}\n", + "..........;\n", + "519 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.5, 'criteria': 4.5}\n", + "..........;\n", + "520 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.6, 'criteria': -5.0}\n", + "..........;\n", + "521 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.6, 'criteria': -4.5}\n", + "..........;\n", + "522 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.6, 'criteria': -4.0}\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "..........;\n", + "523 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.6, 'criteria': -3.5}\n", + "..........;\n", + "524 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.6, 'criteria': -3.0}\n", + "..........;\n", + "525 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.6, 'criteria': -2.5}\n", + "..........;\n", + "526 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.6, 'criteria': -2.0}\n", + "..........;\n", + "527 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.6, 'criteria': -1.5}\n", + "..........;\n", + "528 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.6, 'criteria': -1.0}\n", + "..........;\n", + "529 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.6, 'criteria': -0.5}\n", + "..........;\n", + "530 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.6, 'criteria': 0.0}\n", + "..........;\n", + "531 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.6, 'criteria': 0.5}\n", + "..........;\n", + "532 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.6, 'criteria': 1.0}\n", + "..........;\n", + "533 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.6, 'criteria': 1.5}\n", + "..........;\n", + "534 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.6, 'criteria': 2.0}\n", + "..........;\n", + "535 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.6, 'criteria': 2.5}\n", + "..........;\n", + "536 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.6, 'criteria': 3.0}\n", + "..........;\n", + "537 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.6, 'criteria': 3.5}\n", + "..........;\n", + "538 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.6, 'criteria': 4.0}\n", + "..........;\n", + "539 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.6, 'criteria': 4.5}\n", + "..........;\n", + "540 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.7, 'criteria': -5.0}\n", + "..........;\n", + "541 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.7, 'criteria': -4.5}\n", + "..........;\n", + "542 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.7, 'criteria': -4.0}\n", + "..........;\n", + "543 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.7, 'criteria': -3.5}\n", + "..........;\n", + "544 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.7, 'criteria': -3.0}\n", + "..........;\n", + "545 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.7, 'criteria': -2.5}\n", + "..........;\n", + "546 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.7, 'criteria': -2.0}\n", + "..........;\n", + "547 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.7, 'criteria': -1.5}\n", + "..........;\n", + "548 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.7, 'criteria': -1.0}\n", + "..........;\n", + "549 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.7, 'criteria': -0.5}\n", + "..........;\n", + "550 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.7, 'criteria': 0.0}\n", + "..........;\n", + "551 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.7, 'criteria': 0.5}\n", + "..........;\n", + "552 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.7, 'criteria': 1.0}\n", + "..........;\n", + "553 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.7, 'criteria': 1.5}\n", + "..........;\n", + "554 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.7, 'criteria': 2.0}\n", + "..........;\n", + "555 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.7, 'criteria': 2.5}\n", + "..........;\n", + "556 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.7, 'criteria': 3.0}\n", + "..........;\n", + "557 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.7, 'criteria': 3.5}\n", + "..........;\n", + "558 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.7, 'criteria': 4.0}\n", + "..........;\n", + "559 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.7, 'criteria': 4.5}\n", + "..........;\n", + "560 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.8000000000000003, 'criteria': -5.0}\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "..........;\n", + "561 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.8000000000000003, 'criteria': -4.5}\n", + "..........;\n", + "562 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.8000000000000003, 'criteria': -4.0}\n", + "..........;\n", + "563 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.8000000000000003, 'criteria': -3.5}\n", + "..........;\n", + "564 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.8000000000000003, 'criteria': -3.0}\n", + "..........;\n", + "565 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.8000000000000003, 'criteria': -2.5}\n", + "..........;\n", + "566 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.8000000000000003, 'criteria': -2.0}\n", + "..........;\n", + "567 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.8000000000000003, 'criteria': -1.5}\n", + "..........;\n", + "568 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.8000000000000003, 'criteria': -1.0}\n", + "..........;\n", + "569 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.8000000000000003, 'criteria': -0.5}\n", + "..........;\n", + "570 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.8000000000000003, 'criteria': 0.0}\n", + "..........;\n", + "571 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.8000000000000003, 'criteria': 0.5}\n", + "..........;\n", + "572 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.8000000000000003, 'criteria': 1.0}\n", + "..........;\n", + "573 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.8000000000000003, 'criteria': 1.5}\n", + "..........;\n", + "574 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.8000000000000003, 'criteria': 2.0}\n", + "..........;\n", + "575 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.8000000000000003, 'criteria': 2.5}\n", + "..........;\n", + "576 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.8000000000000003, 'criteria': 3.0}\n", + "..........;\n", + "577 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.8000000000000003, 'criteria': 3.5}\n", + "..........;\n", + "578 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.8000000000000003, 'criteria': 4.0}\n", + "..........;\n", + "579 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.8000000000000003, 'criteria': 4.5}\n", + "..........;\n", + "580 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.9000000000000004, 'criteria': -5.0}\n", + "..........;\n", + "581 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.9000000000000004, 'criteria': -4.5}\n", + "..........;\n", + "582 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.9000000000000004, 'criteria': -4.0}\n", + "..........;\n", + "583 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.9000000000000004, 'criteria': -3.5}\n", + "..........;\n", + "584 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.9000000000000004, 'criteria': -3.0}\n", + "..........;\n", + "585 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.9000000000000004, 'criteria': -2.5}\n", + "..........;\n", + "586 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.9000000000000004, 'criteria': -2.0}\n", + "..........;\n", + "587 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.9000000000000004, 'criteria': -1.5}\n", + "..........;\n", + "588 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.9000000000000004, 'criteria': -1.0}\n", + "..........;\n", + "589 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.9000000000000004, 'criteria': -0.5}\n", + "..........;\n", + "590 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.9000000000000004, 'criteria': 0.0}\n", + "..........;\n", + "591 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.9000000000000004, 'criteria': 0.5}\n", + "..........;\n", + "592 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.9000000000000004, 'criteria': 1.0}\n", + "..........;\n", + "593 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.9000000000000004, 'criteria': 1.5}\n", + "..........;\n", + "594 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.9000000000000004, 'criteria': 2.0}\n", + "..........;\n", + "595 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.9000000000000004, 'criteria': 2.5}\n", + "..........;\n", + "596 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.9000000000000004, 'criteria': 3.0}\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "..........;\n", + "597 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.9000000000000004, 'criteria': 3.5}\n", + "..........;\n", + "598 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.9000000000000004, 'criteria': 4.0}\n", + "..........;\n", + "599 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.9000000000000004, 'criteria': 4.5}\n", + "..........;\n", + "600 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.0, 'criteria': -5.0}\n", + "..........;\n", + "601 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.0, 'criteria': -4.5}\n", + "..........;\n", + "602 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.0, 'criteria': -4.0}\n", + "..........;\n", + "603 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.0, 'criteria': -3.5}\n", + "..........;\n", + "604 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.0, 'criteria': -3.0}\n", + "..........;\n", + "605 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.0, 'criteria': -2.5}\n", + "..........;\n", + "606 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.0, 'criteria': -2.0}\n", + "..........;\n", + "607 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.0, 'criteria': -1.5}\n", + "..........;\n", + "608 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.0, 'criteria': -1.0}\n", + "..........;\n", + "609 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.0, 'criteria': -0.5}\n", + "..........;\n", + "610 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.0, 'criteria': 0.0}\n", + "..........;\n", + "611 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.0, 'criteria': 0.5}\n", + "..........;\n", + "612 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.0, 'criteria': 1.0}\n", + "..........;\n", + "613 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.0, 'criteria': 1.5}\n", + "..........;\n", + "614 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.0, 'criteria': 2.0}\n", + "..........;\n", + "615 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.0, 'criteria': 2.5}\n", + "..........;\n", + "616 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.0, 'criteria': 3.0}\n", + "..........;\n", + "617 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.0, 'criteria': 3.5}\n", + "..........;\n", + "618 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.0, 'criteria': 4.0}\n", + "..........;\n", + "619 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.0, 'criteria': 4.5}\n", + "..........;\n", + "620 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.1, 'criteria': -5.0}\n", + "..........;\n", + "621 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.1, 'criteria': -4.5}\n", + "..........;\n", + "622 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.1, 'criteria': -4.0}\n", + "..........;\n", + "623 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.1, 'criteria': -3.5}\n", + "..........;\n", + "624 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.1, 'criteria': -3.0}\n", + "..........;\n", + "625 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.1, 'criteria': -2.5}\n", + "..........;\n", + "626 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.1, 'criteria': -2.0}\n", + "..........;\n", + "627 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.1, 'criteria': -1.5}\n", + "..........;\n", + "628 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.1, 'criteria': -1.0}\n", + "..........;\n", + "629 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.1, 'criteria': -0.5}\n", + "..........;\n", + "630 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.1, 'criteria': 0.0}\n", + "..........;\n", + "631 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.1, 'criteria': 0.5}\n", + "..........;\n", + "632 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.1, 'criteria': 1.0}\n", + "..........;\n", + "633 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.1, 'criteria': 1.5}\n", + "..........;\n", + "634 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.1, 'criteria': 2.0}\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "..........;\n", + "635 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.1, 'criteria': 2.5}\n", + "..........;\n", + "636 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.1, 'criteria': 3.0}\n", + "..........;\n", + "637 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.1, 'criteria': 3.5}\n", + "..........;\n", + "638 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.1, 'criteria': 4.0}\n", + "..........;\n", + "639 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.1, 'criteria': 4.5}\n", + "..........;\n", + "640 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.2, 'criteria': -5.0}\n", + "..........;\n", + "641 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.2, 'criteria': -4.5}\n", + "..........;\n", + "642 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.2, 'criteria': -4.0}\n", + "..........;\n", + "643 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.2, 'criteria': -3.5}\n", + "..........;\n", + "644 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.2, 'criteria': -3.0}\n", + "..........;\n", + "645 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.2, 'criteria': -2.5}\n", + "..........;\n", + "646 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.2, 'criteria': -2.0}\n", + "..........;\n", + "647 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.2, 'criteria': -1.5}\n", + "..........;\n", + "648 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.2, 'criteria': -1.0}\n", + "..........;\n", + "649 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.2, 'criteria': -0.5}\n", + "..........;\n", + "650 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.2, 'criteria': 0.0}\n", + "..........;\n", + "651 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.2, 'criteria': 0.5}\n", + "..........;\n", + "652 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.2, 'criteria': 1.0}\n", + "..........;\n", + "653 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.2, 'criteria': 1.5}\n", + "..........;\n", + "654 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.2, 'criteria': 2.0}\n", + "..........;\n", + "655 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.2, 'criteria': 2.5}\n", + "..........;\n", + "656 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.2, 'criteria': 3.0}\n", + "..........;\n", + "657 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.2, 'criteria': 3.5}\n", + "..........;\n", + "658 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.2, 'criteria': 4.0}\n", + "..........;\n", + "659 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.2, 'criteria': 4.5}\n", + "..........;\n", + "660 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.3000000000000003, 'criteria': -5.0}\n", + "..........;\n", + "661 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.3000000000000003, 'criteria': -4.5}\n", + "..........;\n", + "662 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.3000000000000003, 'criteria': -4.0}\n", + "..........;\n", + "663 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.3000000000000003, 'criteria': -3.5}\n", + "..........;\n", + "664 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.3000000000000003, 'criteria': -3.0}\n", + "..........;\n", + "665 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.3000000000000003, 'criteria': -2.5}\n", + "..........;\n", + "666 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.3000000000000003, 'criteria': -2.0}\n", + "..........;\n", + "667 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.3000000000000003, 'criteria': -1.5}\n", + "..........;\n", + "668 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.3000000000000003, 'criteria': -1.0}\n", + "..........;\n", + "669 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.3000000000000003, 'criteria': -0.5}\n", + "..........;\n", + "670 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.3000000000000003, 'criteria': 0.0}\n", + "..........;\n", + "671 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.3000000000000003, 'criteria': 0.5}\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "..........;\n", + "672 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.3000000000000003, 'criteria': 1.0}\n", + "..........;\n", + "673 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.3000000000000003, 'criteria': 1.5}\n", + "..........;\n", + "674 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.3000000000000003, 'criteria': 2.0}\n", + "..........;\n", + "675 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.3000000000000003, 'criteria': 2.5}\n", + "..........;\n", + "676 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.3000000000000003, 'criteria': 3.0}\n", + "..........;\n", + "677 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.3000000000000003, 'criteria': 3.5}\n", + "..........;\n", + "678 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.3000000000000003, 'criteria': 4.0}\n", + "..........;\n", + "679 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.3000000000000003, 'criteria': 4.5}\n", + "..........;\n", + "680 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.4000000000000004, 'criteria': -5.0}\n", + "..........;\n", + "681 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.4000000000000004, 'criteria': -4.5}\n", + "..........;\n", + "682 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.4000000000000004, 'criteria': -4.0}\n", + "..........;\n", + "683 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.4000000000000004, 'criteria': -3.5}\n", + "..........;\n", + "684 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.4000000000000004, 'criteria': -3.0}\n", + "..........;\n", + "685 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.4000000000000004, 'criteria': -2.5}\n", + "..........;\n", + "686 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.4000000000000004, 'criteria': -2.0}\n", + "..........;\n", + "687 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.4000000000000004, 'criteria': -1.5}\n", + "..........;\n", + "688 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.4000000000000004, 'criteria': -1.0}\n", + "..........;\n", + "689 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.4000000000000004, 'criteria': -0.5}\n", + "..........;\n", + "690 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.4000000000000004, 'criteria': 0.0}\n", + "..........;\n", + "691 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.4000000000000004, 'criteria': 0.5}\n", + "..........;\n", + "692 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.4000000000000004, 'criteria': 1.0}\n", + "..........;\n", + "693 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.4000000000000004, 'criteria': 1.5}\n", + "..........;\n", + "694 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.4000000000000004, 'criteria': 2.0}\n", + "..........;\n", + "695 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.4000000000000004, 'criteria': 2.5}\n", + "..........;\n", + "696 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.4000000000000004, 'criteria': 3.0}\n", + "..........;\n", + "697 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.4000000000000004, 'criteria': 3.5}\n", + "..........;\n", + "698 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.4000000000000004, 'criteria': 4.0}\n", + "..........;\n", + "699 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.4000000000000004, 'criteria': 4.5}\n", + "..........;\n", + "700 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.5, 'criteria': -5.0}\n", + "..........;\n", + "701 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.5, 'criteria': -4.5}\n", + "..........;\n", + "702 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.5, 'criteria': -4.0}\n", + "..........;\n", + "703 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.5, 'criteria': -3.5}\n", + "..........;\n", + "704 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.5, 'criteria': -3.0}\n", + "..........;\n", + "705 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.5, 'criteria': -2.5}\n", + "..........;\n", + "706 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.5, 'criteria': -2.0}\n", + "..........;\n", + "707 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.5, 'criteria': -1.5}\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "..........;\n", + "708 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.5, 'criteria': -1.0}\n", + "..........;\n", + "709 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.5, 'criteria': -0.5}\n", + "..........;\n", + "710 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.5, 'criteria': 0.0}\n", + "..........;\n", + "711 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.5, 'criteria': 0.5}\n", + "..........;\n", + "712 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.5, 'criteria': 1.0}\n", + "..........;\n", + "713 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.5, 'criteria': 1.5}\n", + "..........;\n", + "714 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.5, 'criteria': 2.0}\n", + "..........;\n", + "715 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.5, 'criteria': 2.5}\n", + "..........;\n", + "716 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.5, 'criteria': 3.0}\n", + "..........;\n", + "717 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.5, 'criteria': 3.5}\n", + "..........;\n", + "718 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.5, 'criteria': 4.0}\n", + "..........;\n", + "719 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.5, 'criteria': 4.5}\n", + "..........;\n", + "720 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.6, 'criteria': -5.0}\n", + "..........;\n", + "721 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.6, 'criteria': -4.5}\n", + "..........;\n", + "722 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.6, 'criteria': -4.0}\n", + "..........;\n", + "723 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.6, 'criteria': -3.5}\n", + "..........;\n", + "724 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.6, 'criteria': -3.0}\n", + "..........;\n", + "725 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.6, 'criteria': -2.5}\n", + "..........;\n", + "726 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.6, 'criteria': -2.0}\n", + "..........;\n", + "727 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.6, 'criteria': -1.5}\n", + "..........;\n", + "728 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.6, 'criteria': -1.0}\n", + "..........;\n", + "729 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.6, 'criteria': -0.5}\n", + "..........;\n", + "730 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.6, 'criteria': 0.0}\n", + "..........;\n", + "731 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.6, 'criteria': 0.5}\n", + "..........;\n", + "732 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.6, 'criteria': 1.0}\n", + "..........;\n", + "733 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.6, 'criteria': 1.5}\n", + "..........;\n", + "734 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.6, 'criteria': 2.0}\n", + "..........;\n", + "735 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.6, 'criteria': 2.5}\n", + "..........;\n", + "736 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.6, 'criteria': 3.0}\n", + "..........;\n", + "737 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.6, 'criteria': 3.5}\n", + "..........;\n", + "738 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.6, 'criteria': 4.0}\n", + "..........;\n", + "739 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.6, 'criteria': 4.5}\n", + "..........;\n", + "740 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.7, 'criteria': -5.0}\n", + "..........;\n", + "741 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.7, 'criteria': -4.5}\n", + "..........;\n", + "742 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.7, 'criteria': -4.0}\n", + "..........;\n", + "743 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.7, 'criteria': -3.5}\n", + "..........;\n", + "744 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.7, 'criteria': -3.0}\n", + "..........;\n", + "745 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.7, 'criteria': -2.5}\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "..........;\n", + "746 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.7, 'criteria': -2.0}\n", + "..........;\n", + "747 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.7, 'criteria': -1.5}\n", + "..........;\n", + "748 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.7, 'criteria': -1.0}\n", + "..........;\n", + "749 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.7, 'criteria': -0.5}\n", + "..........;\n", + "750 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.7, 'criteria': 0.0}\n", + "..........;\n", + "751 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.7, 'criteria': 0.5}\n", + "..........;\n", + "752 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.7, 'criteria': 1.0}\n", + "..........;\n", + "753 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.7, 'criteria': 1.5}\n", + "..........;\n", + "754 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.7, 'criteria': 2.0}\n", + "..........;\n", + "755 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.7, 'criteria': 2.5}\n", + "..........;\n", + "756 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.7, 'criteria': 3.0}\n", + "..........;\n", + "757 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.7, 'criteria': 3.5}\n", + "..........;\n", + "758 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.7, 'criteria': 4.0}\n", + "..........;\n", + "759 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.7, 'criteria': 4.5}\n", + "..........;\n", + "760 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.8000000000000003, 'criteria': -5.0}\n", + "..........;\n", + "761 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.8000000000000003, 'criteria': -4.5}\n", + "..........;\n", + "762 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.8000000000000003, 'criteria': -4.0}\n", + "..........;\n", + "763 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.8000000000000003, 'criteria': -3.5}\n", + "..........;\n", + "764 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.8000000000000003, 'criteria': -3.0}\n", + "..........;\n", + "765 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.8000000000000003, 'criteria': -2.5}\n", + "..........;\n", + "766 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.8000000000000003, 'criteria': -2.0}\n", + "..........;\n", + "767 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.8000000000000003, 'criteria': -1.5}\n", + "..........;\n", + "768 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.8000000000000003, 'criteria': -1.0}\n", + "..........;\n", + "769 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.8000000000000003, 'criteria': -0.5}\n", + "..........;\n", + "770 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.8000000000000003, 'criteria': 0.0}\n", + "..........;\n", + "771 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.8000000000000003, 'criteria': 0.5}\n", + "..........;\n", + "772 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.8000000000000003, 'criteria': 1.0}\n", + "..........;\n", + "773 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.8000000000000003, 'criteria': 1.5}\n", + "..........;\n", + "774 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.8000000000000003, 'criteria': 2.0}\n", + "..........;\n", + "775 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.8000000000000003, 'criteria': 2.5}\n", + "..........;\n", + "776 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.8000000000000003, 'criteria': 3.0}\n", + "..........;\n", + "777 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.8000000000000003, 'criteria': 3.5}\n", + "..........;\n", + "778 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.8000000000000003, 'criteria': 4.0}\n", + "..........;\n", + "779 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.8000000000000003, 'criteria': 4.5}\n", + "..........;\n", + "780 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.9000000000000004, 'criteria': -5.0}\n", + "..........;\n", + "781 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.9000000000000004, 'criteria': -4.5}\n", + "..........;\n", + "782 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.9000000000000004, 'criteria': -4.0}\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "..........;\n", + "783 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.9000000000000004, 'criteria': -3.5}\n", + "..........;\n", + "784 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.9000000000000004, 'criteria': -3.0}\n", + "..........;\n", + "785 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.9000000000000004, 'criteria': -2.5}\n", + "..........;\n", + "786 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.9000000000000004, 'criteria': -2.0}\n", + "..........;\n", + "787 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.9000000000000004, 'criteria': -1.5}\n", + "..........;\n", + "788 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.9000000000000004, 'criteria': -1.0}\n", + "..........;\n", + "789 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.9000000000000004, 'criteria': -0.5}\n", + "..........;\n", + "790 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.9000000000000004, 'criteria': 0.0}\n", + "..........;\n", + "791 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.9000000000000004, 'criteria': 0.5}\n", + "..........;\n", + "792 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.9000000000000004, 'criteria': 1.0}\n", + "..........;\n", + "793 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.9000000000000004, 'criteria': 1.5}\n", + "..........;\n", + "794 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.9000000000000004, 'criteria': 2.0}\n", + "..........;\n", + "795 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.9000000000000004, 'criteria': 2.5}\n", + "..........;\n", + "796 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.9000000000000004, 'criteria': 3.0}\n", + "..........;\n", + "797 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.9000000000000004, 'criteria': 3.5}\n", + "..........;\n", + "798 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.9000000000000004, 'criteria': 4.0}\n", + "..........;\n", + "799 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.9000000000000004, 'criteria': 4.5}\n", + "..........;\n", + "800 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.0, 'criteria': -5.0}\n", + "..........;\n", + "801 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.0, 'criteria': -4.5}\n", + "..........;\n", + "802 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.0, 'criteria': -4.0}\n", + "..........;\n", + "803 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.0, 'criteria': -3.5}\n", + "..........;\n", + "804 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.0, 'criteria': -3.0}\n", + "..........;\n", + "805 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.0, 'criteria': -2.5}\n", + "..........;\n", + "806 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.0, 'criteria': -2.0}\n", + "..........;\n", + "807 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.0, 'criteria': -1.5}\n", + "..........;\n", + "808 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.0, 'criteria': -1.0}\n", + "..........;\n", + "809 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.0, 'criteria': -0.5}\n", + "..........;\n", + "810 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.0, 'criteria': 0.0}\n", + "..........;\n", + "811 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.0, 'criteria': 0.5}\n", + "..........;\n", + "812 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.0, 'criteria': 1.0}\n", + "..........;\n", + "813 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.0, 'criteria': 1.5}\n", + "..........;\n", + "814 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.0, 'criteria': 2.0}\n", + "..........;\n", + "815 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.0, 'criteria': 2.5}\n", + "..........;\n", + "816 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.0, 'criteria': 3.0}\n", + "..........;\n", + "817 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.0, 'criteria': 3.5}\n", + "..........;\n", + "818 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.0, 'criteria': 4.0}\n", + "..........;\n", + "819 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.0, 'criteria': 4.5}\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "..........;\n", + "820 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.1000000000000005, 'criteria': -5.0}\n", + "..........;\n", + "821 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.1000000000000005, 'criteria': -4.5}\n", + "..........;\n", + "822 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.1000000000000005, 'criteria': -4.0}\n", + "..........;\n", + "823 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.1000000000000005, 'criteria': -3.5}\n", + "..........;\n", + "824 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.1000000000000005, 'criteria': -3.0}\n", + "..........;\n", + "825 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.1000000000000005, 'criteria': -2.5}\n", + "..........;\n", + "826 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.1000000000000005, 'criteria': -2.0}\n", + "..........;\n", + "827 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.1000000000000005, 'criteria': -1.5}\n", + "..........;\n", + "828 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.1000000000000005, 'criteria': -1.0}\n", + "..........;\n", + "829 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.1000000000000005, 'criteria': -0.5}\n", + "..........;\n", + "830 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.1000000000000005, 'criteria': 0.0}\n", + "..........;\n", + "831 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.1000000000000005, 'criteria': 0.5}\n", + "..........;\n", + "832 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.1000000000000005, 'criteria': 1.0}\n", + "..........;\n", + "833 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.1000000000000005, 'criteria': 1.5}\n", + "..........;\n", + "834 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.1000000000000005, 'criteria': 2.0}\n", + "..........;\n", + "835 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.1000000000000005, 'criteria': 2.5}\n", + "..........;\n", + "836 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.1000000000000005, 'criteria': 3.0}\n", + "..........;\n", + "837 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.1000000000000005, 'criteria': 3.5}\n", + "..........;\n", + "838 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.1000000000000005, 'criteria': 4.0}\n", + "..........;\n", + "839 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.1000000000000005, 'criteria': 4.5}\n", + "..........;\n", + "840 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.2, 'criteria': -5.0}\n", + "..........;\n", + "841 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.2, 'criteria': -4.5}\n", + "..........;\n", + "842 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.2, 'criteria': -4.0}\n", + "..........;\n", + "843 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.2, 'criteria': -3.5}\n", + "..........;\n", + "844 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.2, 'criteria': -3.0}\n", + "..........;\n", + "845 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.2, 'criteria': -2.5}\n", + "..........;\n", + "846 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.2, 'criteria': -2.0}\n", + "..........;\n", + "847 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.2, 'criteria': -1.5}\n", + "..........;\n", + "848 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.2, 'criteria': -1.0}\n", + "..........;\n", + "849 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.2, 'criteria': -0.5}\n", + "..........;\n", + "850 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.2, 'criteria': 0.0}\n", + "..........;\n", + "851 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.2, 'criteria': 0.5}\n", + "..........;\n", + "852 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.2, 'criteria': 1.0}\n", + "..........;\n", + "853 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.2, 'criteria': 1.5}\n", + "..........;\n", + "854 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.2, 'criteria': 2.0}\n", + "..........;\n", + "855 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.2, 'criteria': 2.5}\n", + "..........;\n", + "856 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.2, 'criteria': 3.0}\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "..........;\n", + "857 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.2, 'criteria': 3.5}\n", + "..........;\n", + "858 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.2, 'criteria': 4.0}\n", + "..........;\n", + "859 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.2, 'criteria': 4.5}\n", + "..........;\n", + "860 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.3, 'criteria': -5.0}\n", + "..........;\n", + "861 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.3, 'criteria': -4.5}\n", + "..........;\n", + "862 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.3, 'criteria': -4.0}\n", + "..........;\n", + "863 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.3, 'criteria': -3.5}\n", + "..........;\n", + "864 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.3, 'criteria': -3.0}\n", + "..........;\n", + "865 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.3, 'criteria': -2.5}\n", + "..........;\n", + "866 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.3, 'criteria': -2.0}\n", + "..........;\n", + "867 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.3, 'criteria': -1.5}\n", + "..........;\n", + "868 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.3, 'criteria': -1.0}\n", + "..........;\n", + "869 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.3, 'criteria': -0.5}\n", + "..........;\n", + "870 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.3, 'criteria': 0.0}\n", + "..........;\n", + "871 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.3, 'criteria': 0.5}\n", + "..........;\n", + "872 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.3, 'criteria': 1.0}\n", + "..........;\n", + "873 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.3, 'criteria': 1.5}\n", + "..........;\n", + "874 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.3, 'criteria': 2.0}\n", + "..........;\n", + "875 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.3, 'criteria': 2.5}\n", + "..........;\n", + "876 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.3, 'criteria': 3.0}\n", + "..........;\n", + "877 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.3, 'criteria': 3.5}\n", + "..........;\n", + "878 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.3, 'criteria': 4.0}\n", + "..........;\n", + "879 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.3, 'criteria': 4.5}\n", + "..........;\n", + "880 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.4, 'criteria': -5.0}\n", + "..........;\n", + "881 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.4, 'criteria': -4.5}\n", + "..........;\n", + "882 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.4, 'criteria': -4.0}\n", + "..........;\n", + "883 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.4, 'criteria': -3.5}\n", + "..........;\n", + "884 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.4, 'criteria': -3.0}\n", + "..........;\n", + "885 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.4, 'criteria': -2.5}\n", + "..........;\n", + "886 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.4, 'criteria': -2.0}\n", + "..........;\n", + "887 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.4, 'criteria': -1.5}\n", + "..........;\n", + "888 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.4, 'criteria': -1.0}\n", + "..........;\n", + "889 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.4, 'criteria': -0.5}\n", + "..........;\n", + "890 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.4, 'criteria': 0.0}\n", + "..........;\n", + "891 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.4, 'criteria': 0.5}\n", + "..........;\n", + "892 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.4, 'criteria': 1.0}\n", + "..........;\n", + "893 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.4, 'criteria': 1.5}\n", + "..........;\n", + "894 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.4, 'criteria': 2.0}\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "..........;\n", + "895 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.4, 'criteria': 2.5}\n", + "..........;\n", + "896 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.4, 'criteria': 3.0}\n", + "..........;\n", + "897 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.4, 'criteria': 3.5}\n", + "..........;\n", + "898 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.4, 'criteria': 4.0}\n", + "..........;\n", + "899 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.4, 'criteria': 4.5}\n", + "..........;\n", + "900 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.5, 'criteria': -5.0}\n", + "..........;\n", + "901 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.5, 'criteria': -4.5}\n", + "..........;\n", + "902 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.5, 'criteria': -4.0}\n", + "..........;\n", + "903 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.5, 'criteria': -3.5}\n", + "..........;\n", + "904 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.5, 'criteria': -3.0}\n", + "..........;\n", + "905 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.5, 'criteria': -2.5}\n", + "..........;\n", + "906 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.5, 'criteria': -2.0}\n", + "..........;\n", + "907 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.5, 'criteria': -1.5}\n", + "..........;\n", + "908 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.5, 'criteria': -1.0}\n", + "..........;\n", + "909 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.5, 'criteria': -0.5}\n", + "..........;\n", + "910 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.5, 'criteria': 0.0}\n", + "..........;\n", + "911 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.5, 'criteria': 0.5}\n", + "..........;\n", + "912 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.5, 'criteria': 1.0}\n", + "..........;\n", + "913 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.5, 'criteria': 1.5}\n", + "..........;\n", + "914 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.5, 'criteria': 2.0}\n", + "..........;\n", + "915 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.5, 'criteria': 2.5}\n", + "..........;\n", + "916 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.5, 'criteria': 3.0}\n", + "..........;\n", + "917 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.5, 'criteria': 3.5}\n", + "..........;\n", + "918 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.5, 'criteria': 4.0}\n", + "..........;\n", + "919 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.5, 'criteria': 4.5}\n", + "..........;\n", + "920 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.6000000000000005, 'criteria': -5.0}\n", + "..........;\n", + "921 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.6000000000000005, 'criteria': -4.5}\n", + "..........;\n", + "922 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.6000000000000005, 'criteria': -4.0}\n", + "..........;\n", + "923 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.6000000000000005, 'criteria': -3.5}\n", + "..........;\n", + "924 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.6000000000000005, 'criteria': -3.0}\n", + "..........;\n", + "925 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.6000000000000005, 'criteria': -2.5}\n", + "..........;\n", + "926 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.6000000000000005, 'criteria': -2.0}\n", + "..........;\n", + "927 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.6000000000000005, 'criteria': -1.5}\n", + "..........;\n", + "928 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.6000000000000005, 'criteria': -1.0}\n", + "..........;\n", + "929 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.6000000000000005, 'criteria': -0.5}\n", + "..........;\n", + "930 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.6000000000000005, 'criteria': 0.0}\n", + "..........;\n", + "931 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.6000000000000005, 'criteria': 0.5}\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "..........;\n", + "932 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.6000000000000005, 'criteria': 1.0}\n", + "..........;\n", + "933 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.6000000000000005, 'criteria': 1.5}\n", + "..........;\n", + "934 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.6000000000000005, 'criteria': 2.0}\n", + "..........;\n", + "935 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.6000000000000005, 'criteria': 2.5}\n", + "..........;\n", + "936 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.6000000000000005, 'criteria': 3.0}\n", + "..........;\n", + "937 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.6000000000000005, 'criteria': 3.5}\n", + "..........;\n", + "938 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.6000000000000005, 'criteria': 4.0}\n", + "..........;\n", + "939 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.6000000000000005, 'criteria': 4.5}\n", + "..........;\n", + "940 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.7, 'criteria': -5.0}\n", + "..........;\n", + "941 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.7, 'criteria': -4.5}\n", + "..........;\n", + "942 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.7, 'criteria': -4.0}\n", + "..........;\n", + "943 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.7, 'criteria': -3.5}\n", + "..........;\n", + "944 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.7, 'criteria': -3.0}\n", + "..........;\n", + "945 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.7, 'criteria': -2.5}\n", + "..........;\n", + "946 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.7, 'criteria': -2.0}\n", + "..........;\n", + "947 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.7, 'criteria': -1.5}\n", + "..........;\n", + "948 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.7, 'criteria': -1.0}\n", + "..........;\n", + "949 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.7, 'criteria': -0.5}\n", + "..........;\n", + "950 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.7, 'criteria': 0.0}\n", + "..........;\n", + "951 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.7, 'criteria': 0.5}\n", + "..........;\n", + "952 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.7, 'criteria': 1.0}\n", + "..........;\n", + "953 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.7, 'criteria': 1.5}\n", + "..........;\n", + "954 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.7, 'criteria': 2.0}\n", + "..........;\n", + "955 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.7, 'criteria': 2.5}\n", + "..........;\n", + "956 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.7, 'criteria': 3.0}\n", + "..........;\n", + "957 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.7, 'criteria': 3.5}\n", + "..........;\n", + "958 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.7, 'criteria': 4.0}\n", + "..........;\n", + "959 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.7, 'criteria': 4.5}\n", + "..........;\n", + "960 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.800000000000001, 'criteria': -5.0}\n", + "..........;\n", + "961 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.800000000000001, 'criteria': -4.5}\n", + "..........;\n", + "962 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.800000000000001, 'criteria': -4.0}\n", + "..........;\n", + "963 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.800000000000001, 'criteria': -3.5}\n", + "..........;\n", + "964 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.800000000000001, 'criteria': -3.0}\n", + "..........;\n", + "965 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.800000000000001, 'criteria': -2.5}\n", + "..........;\n", + "966 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.800000000000001, 'criteria': -2.0}\n", + "..........;\n", + "967 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.800000000000001, 'criteria': -1.5}\n", + "..........;\n", + "968 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.800000000000001, 'criteria': -1.0}\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "..........;\n", + "969 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.800000000000001, 'criteria': -0.5}\n", + "..........;\n", + "970 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.800000000000001, 'criteria': 0.0}\n", + "..........;\n", + "971 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.800000000000001, 'criteria': 0.5}\n", + "..........;\n", + "972 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.800000000000001, 'criteria': 1.0}\n", + "..........;\n", + "973 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.800000000000001, 'criteria': 1.5}\n", + "..........;\n", + "974 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.800000000000001, 'criteria': 2.0}\n", + "..........;\n", + "975 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.800000000000001, 'criteria': 2.5}\n", + "..........;\n", + "976 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.800000000000001, 'criteria': 3.0}\n", + "..........;\n", + "977 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.800000000000001, 'criteria': 3.5}\n", + "..........;\n", + "978 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.800000000000001, 'criteria': 4.0}\n", + "..........;\n", + "979 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.800000000000001, 'criteria': 4.5}\n", + "..........;\n", + "980 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.9, 'criteria': -5.0}\n", + "..........;\n", + "981 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.9, 'criteria': -4.5}\n", + "..........;\n", + "982 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.9, 'criteria': -4.0}\n", + "..........;\n", + "983 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.9, 'criteria': -3.5}\n", + "..........;\n", + "984 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.9, 'criteria': -3.0}\n", + "..........;\n", + "985 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.9, 'criteria': -2.5}\n", + "..........;\n", + "986 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.9, 'criteria': -2.0}\n", + "..........;\n", + "987 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.9, 'criteria': -1.5}\n", + "..........;\n", + "988 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.9, 'criteria': -1.0}\n", + "..........;\n", + "989 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.9, 'criteria': -0.5}\n", + "..........;\n", + "990 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.9, 'criteria': 0.0}\n", + "..........;\n", + "991 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.9, 'criteria': 0.5}\n", + "..........;\n", + "992 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.9, 'criteria': 1.0}\n", + "..........;\n", + "993 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.9, 'criteria': 1.5}\n", + "..........;\n", + "994 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.9, 'criteria': 2.0}\n", + "..........;\n", + "995 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.9, 'criteria': 2.5}\n", + "..........;\n", + "996 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.9, 'criteria': 3.0}\n", + "..........;\n", + "997 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.9, 'criteria': 3.5}\n", + "..........;\n", + "998 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.9, 'criteria': 4.0}\n", + "..........;\n", + "999 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.9, 'criteria': 4.5}\n", + "..........;\n" + ] + }, + { + "ename": "TypeError", + "evalue": "get_metric_names() missing 1 required positional argument: 'self'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[219], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m model \u001b[38;5;241m=\u001b[39m \u001b[43mDoublePass\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbuild_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43minternal_noise_range\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43marange\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m5\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m.1\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 2\u001b[0m \u001b[43m \u001b[49m\u001b[43mcriteria_range\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43marange\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m5\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m5\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m.5\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 3\u001b[0m \u001b[43m \u001b[49m\u001b[43mn_repeated_trials\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1000\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn_runs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m10\u001b[39;49m\u001b[43m)\u001b[49m\n", + "File \u001b[1;32mE:\\WORK\\DO\\2022\\palin\\python\\palin\\internal_noise\\double_pass.py:157\u001b[0m, in \u001b[0;36mDoublePass.build_model\u001b[1;34m(cls, internal_noise_range, criteria_range, n_repeated_trials, n_runs)\u001b[0m\n\u001b[0;32m 154\u001b[0m sim_df \u001b[38;5;241m=\u001b[39m sim\u001b[38;5;241m.\u001b[39mrun_all(n_runs\u001b[38;5;241m=\u001b[39mn_runs, verbose\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m 156\u001b[0m \u001b[38;5;66;03m# average measures over all runs\u001b[39;00m\n\u001b[1;32m--> 157\u001b[0m sim_df\u001b[38;5;241m.\u001b[39mgroupby([\u001b[38;5;124m'\u001b[39m\u001b[38;5;124minternal_noise_std\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcriteria\u001b[39m\u001b[38;5;124m'\u001b[39m])[\u001b[43mDoublePassStatistics\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_metric_names\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m]\u001b[38;5;241m.\u001b[39mmean()\n\u001b[0;32m 158\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m sim_df\n", + "\u001b[1;31mTypeError\u001b[0m: get_metric_names() missing 1 required positional argument: 'self'" + ] + } + ], + "source": [ + "model = DoublePass.build_model(internal_noise_range=np.arange(0,5,.1),\n", + " criteria_range=np.arange(-5,5,.5),\n", + " n_repeated_trials=1000, n_runs=10)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "94a3c61d", + "metadata": {}, + "outputs": [], + "source": [ + "model.to_csv('model_large.csv')" ] }, { "cell_type": "markdown", - "id": "2629230e", + "id": "f217823b", "metadata": {}, "source": [ "## Simulate with kernels" @@ -372,7 +2502,7 @@ }, { "cell_type": "markdown", - "id": "d808e31a", + "id": "26ee88e2", "metadata": {}, "source": [ "Single run" @@ -381,7 +2511,7 @@ { "cell_type": "code", "execution_count": 104, - "id": "52fc9460", + "id": "986cc8b8", "metadata": { "ExecuteTime": { "end_time": "2024-04-10T12:41:07.781836Z", @@ -417,7 +2547,7 @@ { "cell_type": "code", "execution_count": 15, - "id": "43c24287", + "id": "85ac8a46", "metadata": { "ExecuteTime": { "end_time": "2024-04-10T04:42:23.899935Z", @@ -477,7 +2607,7 @@ { "cell_type": "code", "execution_count": 147, - "id": "918f61b8", + "id": "1e0c8a43", "metadata": { "ExecuteTime": { "end_time": "2024-04-10T15:36:24.243659Z", @@ -758,7 +2888,7 @@ { "cell_type": "code", "execution_count": 17, - "id": "5f7ad1b1", + "id": "3d71bdcd", "metadata": { "ExecuteTime": { "end_time": "2024-04-10T04:42:50.935778Z", @@ -796,7 +2926,7 @@ { "cell_type": "code", "execution_count": 168, - "id": "ea12e662", + "id": "81eef14d", "metadata": { "ExecuteTime": { "end_time": "2024-04-11T04:08:49.331441Z", @@ -834,7 +2964,7 @@ { "cell_type": "code", "execution_count": 174, - "id": "be5177b7", + "id": "caf9471b", "metadata": { "ExecuteTime": { "end_time": "2024-04-11T04:10:11.894810Z", @@ -859,7 +2989,7 @@ { "cell_type": "code", "execution_count": 173, - "id": "e657a304", + "id": "ed2de38c", "metadata": { "ExecuteTime": { "end_time": "2024-04-11T04:09:56.002651Z", @@ -885,7 +3015,7 @@ { "cell_type": "code", "execution_count": 175, - "id": "c4826227", + "id": "48a2e99c", "metadata": { "ExecuteTime": { "end_time": "2024-04-11T04:10:14.250903Z", @@ -1041,7 +3171,7 @@ { "cell_type": "code", "execution_count": null, - "id": "cca19c67", + "id": "f578f871", "metadata": {}, "outputs": [], "source": []