From 76b6feec1e2aaeeb7775cdac44406e5014dbcbc4 Mon Sep 17 00:00:00 2001 From: hedonistrh Date: Sat, 26 Jan 2019 22:20:21 +0100 Subject: [PATCH 1/6] Spec file for conda environment --- spec-file.txt | 123 ++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 123 insertions(+) create mode 100644 spec-file.txt diff --git a/spec-file.txt b/spec-file.txt new file mode 100644 index 0000000..bafc410 --- /dev/null +++ b/spec-file.txt @@ -0,0 +1,123 @@ +# This file may be used to create an environment using: +# $ conda create --name --file +# platform: osx-64 +@EXPLICIT +https://repo.anaconda.com/pkgs/main/osx-64/blas-1.0-mkl.tar.bz2 +https://conda.anaconda.org/conda-forge/osx-64/bzip2-1.0.6-1.tar.bz2 +https://conda.anaconda.org/conda-forge/osx-64/c-ares-1.15.0-h470a237_1.tar.bz2 +https://repo.anaconda.com/pkgs/main/osx-64/ca-certificates-2018.03.07-0.tar.bz2 +https://repo.anaconda.com/pkgs/main/osx-64/intel-openmp-2019.1-144.tar.bz2 +https://conda.anaconda.org/anaconda/osx-64/jpeg-9b-he5867d9_2.tar.bz2 +https://repo.anaconda.com/pkgs/main/osx-64/libcxxabi-4.0.1-hcfea43d_1.tar.bz2 +https://repo.anaconda.com/pkgs/main/osx-64/libgfortran-3.0.1-h93005f0_2.tar.bz2 +https://conda.anaconda.org/conda-forge/osx-64/libgpuarray-0.7.6-h470a237_3.tar.bz2 +https://conda.anaconda.org/anaconda/osx-64/libiconv-1.15-hdd342a3_7.tar.bz2 +https://conda.anaconda.org/anaconda/osx-64/libsodium-1.0.16-h3efe00b_0.tar.bz2 +https://conda.anaconda.org/anaconda/osx-64/pandoc-2.2.3.2-0.tar.bz2 +https://conda.anaconda.org/conda-forge/osx-64/toolchain-2.3.0-0.tar.bz2 +https://repo.anaconda.com/pkgs/main/osx-64/xz-5.2.4-h1de35cc_4.tar.bz2 +https://conda.anaconda.org/conda-forge/osx-64/yaml-0.1.7-h470a237_1.tar.bz2 +https://repo.anaconda.com/pkgs/main/osx-64/zlib-1.2.11-h1de35cc_3.tar.bz2 +https://conda.anaconda.org/conda-forge/osx-64/hdf5-1.10.4-nompi_h5598ddc_1003.tar.bz2 +https://repo.anaconda.com/pkgs/main/osx-64/libcxx-4.0.1-hcfea43d_1.tar.bz2 +https://repo.anaconda.com/pkgs/main/osx-64/libpng-1.6.35-ha441bb4_0.tar.bz2 +https://conda.anaconda.org/conda-forge/osx-64/libprotobuf-3.6.1-hd28b015_0.tar.bz2 +https://repo.anaconda.com/pkgs/main/osx-64/mkl-2019.1-144.tar.bz2 +https://repo.anaconda.com/pkgs/main/osx-64/openssl-1.1.1a-h1de35cc_0.tar.bz2 +https://repo.anaconda.com/pkgs/main/osx-64/tk-8.6.8-ha441bb4_0.tar.bz2 +https://conda.anaconda.org/conda-forge/osx-64/toolchain_c_osx-64-2.3.0-0.tar.bz2 +https://conda.anaconda.org/conda-forge/osx-64/toolchain_cxx_osx-64-2.3.0-0.tar.bz2 +https://conda.anaconda.org/anaconda/osx-64/expat-2.2.6-h0a44026_0.tar.bz2 +https://repo.anaconda.com/pkgs/main/osx-64/freetype-2.9.1-hb4e5f40_0.tar.bz2 +https://conda.anaconda.org/anaconda/osx-64/icu-58.2-h4b95b61_1.tar.bz2 +https://repo.anaconda.com/pkgs/main/osx-64/libffi-3.2.1-h475c297_4.tar.bz2 +https://repo.anaconda.com/pkgs/main/osx-64/ncurses-6.1-h0a44026_1.tar.bz2 +https://conda.anaconda.org/anaconda/osx-64/pcre-8.42-h378b8a2_0.tar.bz2 +https://conda.anaconda.org/anaconda/osx-64/zeromq-4.2.5-h0a44026_1.tar.bz2 +https://conda.anaconda.org/anaconda/osx-64/gettext-0.19.8.1-h15daf44_3.tar.bz2 +https://repo.anaconda.com/pkgs/main/osx-64/libedit-3.1.20170329-hb402a30_2.tar.bz2 +https://repo.anaconda.com/pkgs/main/osx-64/readline-7.0-h1de35cc_5.tar.bz2 +https://conda.anaconda.org/anaconda/osx-64/glib-2.56.2-hd9629dc_0.tar.bz2 +https://repo.anaconda.com/pkgs/main/osx-64/sqlite-3.25.3-ha441bb4_0.tar.bz2 +https://conda.anaconda.org/anaconda/osx-64/dbus-1.13.2-h760590f_1.tar.bz2 +https://repo.anaconda.com/pkgs/main/osx-64/python-3.6.7-haf84260_0.tar.bz2 +https://conda.anaconda.org/anaconda/osx-64/qt-5.9.6-h45cd832_2.tar.bz2 +https://conda.anaconda.org/anaconda/osx-64/appnope-0.1.0-py36hf537a9a_0.tar.bz2 +https://conda.anaconda.org/conda-forge/noarch/astor-0.7.1-py_0.tar.bz2 +https://conda.anaconda.org/anaconda/osx-64/backcall-0.1.0-py36_0.tar.bz2 +https://repo.anaconda.com/pkgs/main/osx-64/certifi-2018.11.29-py36_0.tar.bz2 +https://conda.anaconda.org/anaconda/osx-64/decorator-4.3.0-py36_0.tar.bz2 +https://conda.anaconda.org/anaconda/osx-64/entrypoints-0.2.3-py36_2.tar.bz2 +https://conda.anaconda.org/conda-forge/noarch/gast-0.2.0-py_0.tar.bz2 +https://conda.anaconda.org/anaconda/osx-64/ipython_genutils-0.2.0-py36h241746c_0.tar.bz2 +https://repo.anaconda.com/pkgs/main/osx-64/kiwisolver-1.0.1-py36h0a44026_0.tar.bz2 +https://conda.anaconda.org/conda-forge/noarch/markdown-2.6.11-py_0.tar.bz2 +https://conda.anaconda.org/conda-forge/osx-64/markupsafe-1.1.0-py36h470a237_0.tar.bz2 +https://conda.anaconda.org/anaconda/osx-64/mistune-0.8.4-py36h1de35cc_0.tar.bz2 +https://repo.anaconda.com/pkgs/main/osx-64/numpy-base-1.15.4-py36h6575580_0.tar.bz2 +https://conda.anaconda.org/anaconda/osx-64/pandocfilters-1.4.2-py36_1.tar.bz2 +https://conda.anaconda.org/anaconda/osx-64/parso-0.3.1-py36_0.tar.bz2 +https://conda.anaconda.org/anaconda/osx-64/pickleshare-0.7.5-py36_0.tar.bz2 +https://conda.anaconda.org/anaconda/osx-64/prometheus_client-0.4.2-py36_0.tar.bz2 +https://conda.anaconda.org/anaconda/osx-64/ptyprocess-0.6.0-py36_0.tar.bz2 +https://repo.anaconda.com/pkgs/main/osx-64/pyparsing-2.3.0-py36_0.tar.bz2 +https://repo.anaconda.com/pkgs/main/osx-64/pytz-2018.7-py36_0.tar.bz2 +https://conda.anaconda.org/conda-forge/osx-64/pyyaml-3.13-py36h470a237_1.tar.bz2 +https://conda.anaconda.org/anaconda/osx-64/pyzmq-17.1.2-py36h1de35cc_0.tar.bz2 +https://conda.anaconda.org/anaconda/osx-64/send2trash-1.5.0-py36_0.tar.bz2 +https://conda.anaconda.org/anaconda/osx-64/sip-4.19.13-py36h0a44026_0.tar.bz2 +https://repo.anaconda.com/pkgs/main/osx-64/six-1.12.0-py36_0.tar.bz2 +https://conda.anaconda.org/conda-forge/noarch/termcolor-1.1.0-py_2.tar.bz2 +https://conda.anaconda.org/anaconda/osx-64/testpath-0.4.2-py36_0.tar.bz2 +https://repo.anaconda.com/pkgs/main/osx-64/tornado-5.1.1-py36h1de35cc_0.tar.bz2 +https://conda.anaconda.org/anaconda/osx-64/wcwidth-0.1.7-py36h8c6ec74_0.tar.bz2 +https://conda.anaconda.org/anaconda/osx-64/webencodings-0.5.1-py36_1.tar.bz2 +https://conda.anaconda.org/conda-forge/noarch/werkzeug-0.14.1-py_0.tar.bz2 +https://conda.anaconda.org/conda-forge/osx-64/absl-py-0.6.1-py36_1000.tar.bz2 +https://repo.anaconda.com/pkgs/main/osx-64/cycler-0.10.0-py36hfc81398_0.tar.bz2 +https://conda.anaconda.org/anaconda/osx-64/jedi-0.13.1-py36_0.tar.bz2 +https://conda.anaconda.org/conda-forge/noarch/mako-1.0.7-py_1.tar.bz2 +https://repo.anaconda.com/pkgs/main/osx-64/mkl_fft-1.0.6-py36h27c97d8_0.tar.bz2 +https://repo.anaconda.com/pkgs/main/osx-64/mkl_random-1.0.2-py36h27c97d8_0.tar.bz2 +https://conda.anaconda.org/anaconda/osx-64/pexpect-4.6.0-py36_0.tar.bz2 +https://conda.anaconda.org/anaconda/osx-64/pyqt-5.9.2-py36h655552a_0.tar.bz2 +https://repo.anaconda.com/pkgs/main/osx-64/python-dateutil-2.7.5-py36_0.tar.bz2 +https://repo.anaconda.com/pkgs/main/osx-64/setuptools-40.6.2-py36_0.tar.bz2 +https://conda.anaconda.org/anaconda/osx-64/terminado-0.8.1-py36_1.tar.bz2 +https://conda.anaconda.org/anaconda/osx-64/traitlets-4.3.2-py36h65bd3ce_0.tar.bz2 +https://conda.anaconda.org/anaconda/osx-64/bleach-3.0.2-py36_0.tar.bz2 +https://repo.anaconda.com/pkgs/main/osx-64/grpcio-1.16.1-py36h044775b_1.tar.bz2 +https://conda.anaconda.org/anaconda/osx-64/jinja2-2.10-py36_0.tar.bz2 +https://conda.anaconda.org/anaconda/osx-64/jsonschema-2.6.0-py36hb385e00_0.tar.bz2 +https://conda.anaconda.org/anaconda/osx-64/jupyter_core-4.4.0-py36_0.tar.bz2 +https://repo.anaconda.com/pkgs/main/osx-64/numpy-1.15.4-py36hacdab7b_0.tar.bz2 +https://conda.anaconda.org/conda-forge/osx-64/protobuf-3.6.1-py36hfc679d8_1.tar.bz2 +https://conda.anaconda.org/anaconda/osx-64/pygments-2.2.0-py36h240cd3f_0.tar.bz2 +https://repo.anaconda.com/pkgs/main/osx-64/wheel-0.32.3-py36_0.tar.bz2 +https://conda.anaconda.org/conda-forge/osx-64/h5py-2.8.0-py36he5c79e1_5.tar.bz2 +https://conda.anaconda.org/anaconda/osx-64/jupyter_client-5.2.3-py36_0.tar.bz2 +https://repo.anaconda.com/pkgs/main/osx-64/matplotlib-3.0.2-py36h54f8f79_0.tar.bz2 +https://conda.anaconda.org/anaconda/osx-64/nbformat-4.4.0-py36h827af21_0.tar.bz2 +https://repo.anaconda.com/pkgs/main/osx-64/pandas-0.23.4-py36h6440ff4_0.tar.bz2 +https://repo.anaconda.com/pkgs/main/osx-64/pip-18.1-py36_0.tar.bz2 +https://conda.anaconda.org/anaconda/osx-64/prompt_toolkit-2.0.7-py36_0.tar.bz2 +https://conda.anaconda.org/conda-forge/osx-64/pygpu-0.7.6-py36h7eb728f_0.tar.bz2 +https://repo.anaconda.com/pkgs/main/osx-64/scipy-1.1.0-py36h1410ff5_2.tar.bz2 +https://conda.anaconda.org/conda-forge/osx-64/tensorboard-1.10.0-py36_0.tar.bz2 +https://conda.anaconda.org/anaconda/osx-64/ipython-7.2.0-py36h39e3cac_0.tar.bz2 +https://conda.anaconda.org/jupycon/label/dev/osx-64/nb_conda_kernels-2.2.0+5.gdb8c10d-py36_0.tar.bz2 +https://conda.anaconda.org/anaconda/osx-64/nbconvert-5.3.1-py36_0.tar.bz2 +https://conda.anaconda.org/conda-forge/osx-64/tensorflow-1.10.0-py36_0.tar.bz2 +https://conda.anaconda.org/conda-forge/osx-64/theano-1.0.3-py36hfc679d8_1.tar.bz2 +https://conda.anaconda.org/anaconda/osx-64/ipykernel-5.1.0-py36h39e3cac_0.tar.bz2 +https://conda.anaconda.org/anaconda/osx-64/jupyter_console-6.0.0-py36_0.tar.bz2 +https://conda.anaconda.org/anaconda/osx-64/notebook-5.7.2-py36_0.tar.bz2 +https://conda.anaconda.org/anaconda/osx-64/qtconsole-4.4.2-py36_0.tar.bz2 +https://conda.anaconda.org/conda-forge/noarch/jupyterlab_server-0.2.0-py_0.tar.bz2 +https://conda.anaconda.org/anaconda/osx-64/widgetsnbextension-3.4.2-py36_0.tar.bz2 +https://conda.anaconda.org/anaconda/osx-64/ipywidgets-7.4.2-py36_0.tar.bz2 +https://conda.anaconda.org/conda-forge/osx-64/jupyterlab-0.35.4-py36_0.tar.bz2 +https://conda.anaconda.org/anaconda/osx-64/jupyter-1.0.0-py36_7.tar.bz2 +https://conda.anaconda.org/conda-forge/noarch/keras-applications-1.0.4-py_1.tar.bz2 +https://conda.anaconda.org/conda-forge/osx-64/keras-2.2.4-py36_0.tar.bz2 +https://conda.anaconda.org/conda-forge/noarch/keras-preprocessing-1.0.2-py_1.tar.bz2 From 167c91cb9219a65a667e7015be0965fb9b1a24b4 Mon Sep 17 00:00:00 2001 From: hedonistrh Date: Sat, 26 Jan 2019 22:27:05 +0100 Subject: [PATCH 2/6] Mock Training notebook has been added. --- MockTraining.ipynb | 869 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 869 insertions(+) create mode 100644 MockTraining.ipynb diff --git a/MockTraining.ipynb b/MockTraining.ipynb new file mode 100644 index 0000000..9c978b0 --- /dev/null +++ b/MockTraining.ipynb @@ -0,0 +1,869 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Mock training for the baseline system which is LSTM. For the dataset, we will use hicaz--sarki datas." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Using TensorFlow backend.\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import os\n", + "import numpy as np\n", + "from keras.models import Sequential, Model\n", + "from keras.layers import Dense, Activation, Dropout, GRU\n", + "from keras.layers import LSTM, Input, Bidirectional\n", + "from keras.optimizers import Adam\n", + "from keras.callbacks import EarlyStopping\n", + "from keras.callbacks import ModelCheckpoint\n", + "from keras.metrics import categorical_accuracy\n", + "from keras.layers.embeddings import Embedding\n", + "from keras.layers.recurrent import SimpleRNN\n", + "from keras.layers.wrappers import TimeDistributed\n", + "from keras.layers import Convolution1D\n", + "from keras.models import load_model\n", + "%matplotlib inline \n", + "%config InlineBackend.figure_format = 'retina' # for Mac\n", + "import matplotlib.pyplot as plt\n", + "plt.rcParams['figure.figsize'] = [20, 7]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Download [datas](https://minhaskamal.github.io/DownGit/#/home?url=https://github.com/MTG/SymbTr/tree/master/txt) (which command will download 1000 .txt file from [SymbTR](https://github.com/MTG/SymbTr/tree/master/txt)) and extract from zip. After that, you can follow this notebook." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Upload the datas into panda's dataframe. As I mentioned before, for mock training, we have just used hicaz--sarki datas." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import glob\n", + "root_dir = \"./txt/\"\n", + "root_dir = glob.glob(os.path.join(root_dir, \"*txt\"))\n", + "\n", + "df_hicaz_sarki = pd.concat((pd.read_csv(f, sep=\"\\t\") for f in root_dir if \"hicaz--sarki\" in f))" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"To adjust figure sizes.\"\"\"\n", + "\n", + "import matplotlib.pyplot as plt\n", + "plt.rcParams['figure.figsize'] = [20, 7]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will focus to **generation of Koma53**. Lets look the distribution of Koma53 values.\n", + "\n", + "_Ps. We can also focus KomaAE._" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 422, + "width": 1164 + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "pd.value_counts(df_hicaz_sarki['Koma53']).plot.bar()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total Koma53 value: 40149\n" + ] + } + ], + "source": [ + "total_value = 0\n", + "for key, value in pd.value_counts(df_hicaz_sarki['Koma53']).items():\n", + " total_value += value;\n", + "\n", + "print (\"Total Koma53 value: \", total_value)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will map the uncommon notes into nearest common note to get rid of uncommon note problem. The reason behind that LSTM based system can not learn these uncommon notes. \n", + "\n", + "Ps. We can also discard these uncommon notes, however, we choose mapping." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "def find_nearest(array, value):\n", + " array = np.asarray(array)\n", + " idx = (np.abs(array - value)).argmin()\n", + " return (array[idx])\n", + "\n", + "def map_notes(valueCountsOfNote, threshold):\n", + " \"\"\"This function takes value counts for the Koma53-KomaAe \n", + " and gives the dict to represent which note should be \n", + " transformed into which note to get rid of uncommon\n", + " note problem.\n", + " \n", + " Arguments:\n", + " valueCountsOfNote: Value counts of Koma53 or KomaAe \n", + " threshold: What is the minimum number \n", + " to kept this note as the common note.\"\"\"\n", + " \n", + " upThresKeyList = [] # To store, which key has more value than threshold\n", + " mapDict = {} # This dict's keys store the uncommon notes and value store\n", + " # common notes.\n", + " \n", + " for key, value in valueCountsOfNote.items():\n", + " if (value > threshold):\n", + " upThresKeyList.append(key)\n", + " \n", + " upThresKeyArray = np.asarray(upThresKeyList)\n", + " \n", + " for key, value in valueCountsOfNote.items():\n", + " if key not in upThresKeyList:\n", + " nearNote = find_nearest(upThresKeyArray, key)\n", + " mapDict[key] = nearNote\n", + " return mapDict\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "mappedKoma53Dict = (map_notes(pd.value_counts(df_hicaz_sarki['Koma53']), 250))" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "df_hicaz_sarki[\"Mapped Koma53\"] = df_hicaz_sarki.Koma53.replace(to_replace=mappedKoma53Dict)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 422, + "width": 1164 + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "pd.value_counts(df_hicaz_sarki['Mapped Koma53']).plot.bar()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we can move into **generation** part." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "note_list = df_hicaz_sarki['Mapped Koma53'].tolist()\n", + "note_list = list(map(str, note_list))" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "save_dir = 'save' # To store trained DL model\n", + "vocab_file = os.path.join(save_dir, \"words_vocab.pkl\")" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "vocab size: 15\n" + ] + } + ], + "source": [ + "import collections\n", + "from six.moves import cPickle\n", + "\n", + "# Count the number of words\n", + "word_counts = collections.Counter(note_list)\n", + "\n", + "# Mapping from index to word : that's the vocabulary\n", + "vocabulary_inv = [x[0] for x in word_counts.most_common()]\n", + "vocabulary_inv = list(sorted(vocabulary_inv))\n", + "\n", + "# Mapping from word to index\n", + "vocab = {x: i for i, x in enumerate(vocabulary_inv)}\n", + "words = [x[0] for x in word_counts.most_common()]\n", + "\n", + "# Size of vocabulary\n", + "vocab_size = len(words)\n", + "print(\"vocab size: \", vocab_size)\n", + "\n", + "# Save the words and vocabulary as a pickle file\n", + "with open(os.path.join(vocab_file), 'wb') as f:\n", + " cPickle.dump((words, vocab, vocabulary_inv), f)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "nb sequences: 40143\n" + ] + } + ], + "source": [ + "\"\"\"We need to create two different list. One list include the previous words, \n", + "another list inclued the next word.\"\"\"\n", + "\n", + "seq_length = 6\n", + "sequences_step = 1\n", + "\n", + "sequences = []\n", + "next_words = []\n", + "for i in range(0, len(note_list) - seq_length, sequences_step):\n", + " sequences.append(note_list[i: i + seq_length])\n", + " next_words.append(note_list[i + seq_length])\n", + "\n", + "print('nb sequences:', len(sequences))" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"We can not use this type of array directly. So that, we have to modify this data to use with LSTM. We need \n", + "to convert into one-hot vector type array. \n", + "\n", + "List which includes previous words should have a dimension as number of sequences, number of words in sequences,\n", + "number of words in vocabulary. The other list should have a dimension as number of sequences, \n", + "number of words in vocabulary.\"\"\"\n", + "\n", + "X = np.zeros((len(sequences), seq_length, vocab_size), dtype=np.bool)\n", + "y = np.zeros((len(sequences), vocab_size), dtype=np.bool)\n", + "for i, sentence in enumerate(sequences):\n", + " for t, word in enumerate(sentence):\n", + " X[i, t, vocab[word]] = 1\n", + " y[i, vocab[next_words[i]]] = 1" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "def bidirectional_lstm_model(seq_length, vocab_size):\n", + " print('Build LSTM model.')\n", + " model = Sequential()\n", + " model.add(Bidirectional(LSTM(rnn_size, activation=\"relu\", return_sequences=True, \n", + " kernel_initializer='random_normal',\n", + " bias_initializer='random_normal'),\n", + " input_shape=(seq_length, vocab_size)))\n", + " model.add(Dropout(0.7))\n", + " model.add(Bidirectional(LSTM(rnn_size, activation=\"relu\", return_sequences=True, \n", + " kernel_initializer='random_normal',\n", + " bias_initializer='random_normal')))\n", + " model.add(Dropout(0.7))\n", + " model.add(Bidirectional(LSTM(rnn_size, activation=\"relu\",\n", + " kernel_initializer='random_normal',\n", + " bias_initializer='random_normal')))\n", + " model.add(Dropout(0.3))\n", + " model.add(Dense(vocab_size))\n", + " model.add(Activation('softmax'))\n", + " \n", + " optimizer = Adam(lr=learning_rate)\n", + " callbacks=[EarlyStopping(patience=2, monitor='val_loss')]\n", + " model.compile(loss='categorical_crossentropy', optimizer=optimizer, metrics=[categorical_accuracy])\n", + " print(\"model built!\")\n", + " return model" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Build LSTM model.\n", + "model built!\n", + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "bidirectional_1 (Bidirection (None, 6, 256) 147456 \n", + "_________________________________________________________________\n", + "dropout_1 (Dropout) (None, 6, 256) 0 \n", + "_________________________________________________________________\n", + "bidirectional_2 (Bidirection (None, 6, 256) 394240 \n", + "_________________________________________________________________\n", + "dropout_2 (Dropout) (None, 6, 256) 0 \n", + "_________________________________________________________________\n", + "bidirectional_3 (Bidirection (None, 256) 394240 \n", + "_________________________________________________________________\n", + "dropout_3 (Dropout) (None, 256) 0 \n", + "_________________________________________________________________\n", + "dense_1 (Dense) (None, 15) 3855 \n", + "_________________________________________________________________\n", + "activation_1 (Activation) (None, 15) 0 \n", + "=================================================================\n", + "Total params: 939,791\n", + "Trainable params: 939,791\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "rnn_size = 128 # size of RNN\n", + "learning_rate = 0.001\n", + "\n", + "md = bidirectional_lstm_model(seq_length, vocab_size)\n", + "md.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can **train** the model." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train on 36128 samples, validate on 4015 samples\n", + "Epoch 1/10\n", + "36128/36128 [==============================] - 73s 2ms/step - loss: 1.9022 - categorical_accuracy: 0.3029 - val_loss: 1.5833 - val_categorical_accuracy: 0.4167\n", + "Epoch 2/10\n", + "36128/36128 [==============================] - 64s 2ms/step - loss: 1.6176 - categorical_accuracy: 0.4177 - val_loss: 1.4995 - val_categorical_accuracy: 0.4575\n", + "Epoch 3/10\n", + "36128/36128 [==============================] - 60s 2ms/step - loss: 1.5632 - categorical_accuracy: 0.4442 - val_loss: 1.4713 - val_categorical_accuracy: 0.4767\n", + "Epoch 4/10\n", + "36128/36128 [==============================] - 64s 2ms/step - loss: 1.5324 - categorical_accuracy: 0.4601 - val_loss: 1.4445 - val_categorical_accuracy: 0.4800\n", + "Epoch 5/10\n", + "36128/36128 [==============================] - 59s 2ms/step - loss: 1.5054 - categorical_accuracy: 0.4678 - val_loss: 1.4391 - val_categorical_accuracy: 0.4752\n", + "Epoch 6/10\n", + "36128/36128 [==============================] - 58s 2ms/step - loss: 1.4933 - categorical_accuracy: 0.4728 - val_loss: 1.4246 - val_categorical_accuracy: 0.4909\n", + "Epoch 7/10\n", + "36128/36128 [==============================] - 62s 2ms/step - loss: 1.4711 - categorical_accuracy: 0.4812 - val_loss: 1.4165 - val_categorical_accuracy: 0.4800\n", + "Epoch 8/10\n", + "36128/36128 [==============================] - 58s 2ms/step - loss: 1.4617 - categorical_accuracy: 0.4843 - val_loss: 1.4094 - val_categorical_accuracy: 0.4902\n", + "Epoch 9/10\n", + "36128/36128 [==============================] - 58s 2ms/step - loss: 1.4517 - categorical_accuracy: 0.4877 - val_loss: 1.4006 - val_categorical_accuracy: 0.4986\n", + "Epoch 10/10\n", + "36128/36128 [==============================] - 73s 2ms/step - loss: 1.4405 - categorical_accuracy: 0.4940 - val_loss: 1.3898 - val_categorical_accuracy: 0.4981\n" + ] + } + ], + "source": [ + "batch_size = 64 # minibatch size\n", + "num_epochs = 10 # number of epochs\n", + "\n", + "callbacks=[EarlyStopping(patience=4, monitor='val_loss'),\n", + " ModelCheckpoint(filepath=save_dir + \"/\" + 'my_model_gen_koma53.{epoch:02d}-{val_loss:.2f}.hdf5',\\\n", + " monitor='val_loss', verbose=0, mode='auto', period=2)]\n", + "\n", + "history = md.fit(X, y,\n", + " batch_size=batch_size,\n", + " shuffle=True,\n", + " epochs=num_epochs,\n", + " callbacks=callbacks,\n", + " validation_split=0.1)\n", + "\n", + "md.save(save_dir + \"/\" + 'my_model_generate_koma53.h5')" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 440, + "width": 1181 + }, + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 440, + "width": 1168 + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# summarize history for accuracy\n", + "plt.plot(history.history['categorical_accuracy'])\n", + "plt.plot(history.history['val_categorical_accuracy'])\n", + "plt.title('model accuracy')\n", + "plt.ylabel('accuracy')\n", + "plt.xlabel('epoch')\n", + "plt.legend(['train', 'test'], loc='upper left')\n", + "plt.show()\n", + "# summarize history for loss\n", + "plt.plot(history.history['loss'])\n", + "plt.plot(history.history['val_loss'])\n", + "plt.title('model loss')\n", + "plt.ylabel('loss')\n", + "plt.xlabel('epoch')\n", + "plt.legend(['train', 'test'], loc='upper left')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 426, + "width": 1168 + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Get training and test loss histories\n", + "training_loss = history.history['loss']\n", + "test_loss = history.history['val_loss']\n", + "\n", + "# Create count of the number of epochs\n", + "epoch_count = range(1, len(training_loss) + 1)\n", + "\n", + "# Visualize loss history\n", + "plt.plot(epoch_count, training_loss, 'r--')\n", + "plt.plot(epoch_count, test_loss, 'b-')\n", + "plt.legend(['Training Loss', 'Test Loss'])\n", + "plt.xlabel('Epoch')\n", + "plt.ylabel('Loss')\n", + "plt.show();" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loading vocabulary...\n", + "loading model...\n" + ] + } + ], + "source": [ + "save_dir = \"./save/\"\n", + "\n", + "# Load vocabulary\n", + "print(\"loading vocabulary...\")\n", + "vocab_file = os.path.join(save_dir, \"words_vocab.pkl\")\n", + "\n", + "with open(os.path.join(save_dir, 'words_vocab.pkl'), 'rb') as f:\n", + " words, vocab, vocabulary_inv = cPickle.load(f)\n", + "\n", + "vocab_size = len(words)\n", + "\n", + "# Load the model\n", + "print(\"loading model...\")\n", + "model = load_model(save_dir + \"/\" + 'my_model_generate_sentences.h5')" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "def sample(preds, temperature=1):\n", + " preds = np.asarray(preds).astype('float64')\n", + " preds = np.log(preds) / temperature\n", + " exp_preds = np.exp(preds)\n", + " preds = exp_preds / np.sum(exp_preds)\n", + " probas = np.random.multinomial(1, preds, 1)\n", + " return np.argmax(probas)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Generating text with the following seed: \"336 305 310 358 310 327\"\n", + "\n" + ] + } + ], + "source": [ + "# Iniatate sentence\n", + "seed_sentences = \"327 336 305 310 358 310 344\"\n", + "generated = ''\n", + "sentence = []\n", + "for i in range (seq_length):\n", + " sentence.append(\"312\")\n", + "\n", + "seed = seed_sentences.split()\n", + "\n", + "for i in range(len(seed)):\n", + " sentence[seq_length-i-1]=seed[len(seed)-i-1]\n", + "\n", + "generated += ' '.join(sentence)\n", + "print('Generating text with the following seed: \"' + ' '.join(sentence) + '\"')\n", + "\n", + "print ()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now **generate** an example." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "words_number = 30000\n", + "# Generate the text\n", + "for i in range(words_number):\n", + " #create the vector\n", + " x = np.zeros((1, seq_length, vocab_size))\n", + " for t, word in enumerate(sentence):\n", + " x[0, t, vocab[word]] = 1.\n", + " #print(x.shape)\n", + "\n", + " #calculate next word\n", + " preds = model.predict(x, verbose=0)[0]\n", + " next_index = sample(preds, 0.2)\n", + " next_word = vocabulary_inv[next_index]\n", + "\n", + " #add the next word to the text\n", + " generated += \" \" + next_word\n", + " # shift the sentence by one, and and the next word at its end\n", + " sentence = sentence[1:] + [next_word]\n", + "\n", + "# print(generated)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "generatedNotesList = []\n", + "for singleGenerated in generated.split(\" \"):\n", + " generatedNotesList.append(int(singleGenerated))" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "np.asarray(generatedNotesList)\n", + "arr = np.asarray(generatedNotesList)\n", + "generatedDataFrame = pd.DataFrame({'Koma53':arr})" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 436, + "width": 1164 + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "pd.value_counts(generatedDataFrame['Koma53']).plot(kind='bar', title=\"Dist. of Generated Koma53\")" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 436, + "width": 1164 + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# pd.value_counts(df_hicaz_sarki['Mapped Koma53']).plot.bar()\n", + "pd.value_counts(df_hicaz_sarki['Mapped Koma53']).plot(kind='bar', title=\"Dist. of Original Koma53\")" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + " 336 6207\n", + " 327 5333\n", + " 349 4580\n", + " 344 4182\n", + " 358 3601\n", + " 322 2782\n", + " 310 1314\n", + " 340 1030\n", + " 305 835\n", + " 296 80\n", + " 367 58\n", + "-1 4\n", + "Name: Koma53, dtype: int64" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.value_counts(generatedDataFrame['Koma53'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [conda env:turkishMusicGen] *", + "language": "python", + "name": "conda-env-turkishMusicGen-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 867479979269b9f9c9f4e49c116aa9b8779e8586 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Halil=20Erdo=C4=9Fan?= Date: Sat, 2 Feb 2019 13:49:04 +0100 Subject: [PATCH 3/6] Delete MockTraining.ipynb --- MockTraining.ipynb | 869 --------------------------------------------- 1 file changed, 869 deletions(-) delete mode 100644 MockTraining.ipynb diff --git a/MockTraining.ipynb b/MockTraining.ipynb deleted file mode 100644 index 9c978b0..0000000 --- a/MockTraining.ipynb +++ /dev/null @@ -1,869 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Mock training for the baseline system which is LSTM. For the dataset, we will use hicaz--sarki datas." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Using TensorFlow backend.\n" - ] - } - ], - "source": [ - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "import os\n", - "import numpy as np\n", - "from keras.models import Sequential, Model\n", - "from keras.layers import Dense, Activation, Dropout, GRU\n", - "from keras.layers import LSTM, Input, Bidirectional\n", - "from keras.optimizers import Adam\n", - "from keras.callbacks import EarlyStopping\n", - "from keras.callbacks import ModelCheckpoint\n", - "from keras.metrics import categorical_accuracy\n", - "from keras.layers.embeddings import Embedding\n", - "from keras.layers.recurrent import SimpleRNN\n", - "from keras.layers.wrappers import TimeDistributed\n", - "from keras.layers import Convolution1D\n", - "from keras.models import load_model\n", - "%matplotlib inline \n", - "%config InlineBackend.figure_format = 'retina' # for Mac\n", - "import matplotlib.pyplot as plt\n", - "plt.rcParams['figure.figsize'] = [20, 7]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Download [datas](https://minhaskamal.github.io/DownGit/#/home?url=https://github.com/MTG/SymbTr/tree/master/txt) (which command will download 1000 .txt file from [SymbTR](https://github.com/MTG/SymbTr/tree/master/txt)) and extract from zip. After that, you can follow this notebook." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Upload the datas into panda's dataframe. As I mentioned before, for mock training, we have just used hicaz--sarki datas." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import glob\n", - "root_dir = \"./txt/\"\n", - "root_dir = glob.glob(os.path.join(root_dir, \"*txt\"))\n", - "\n", - "df_hicaz_sarki = pd.concat((pd.read_csv(f, sep=\"\\t\") for f in root_dir if \"hicaz--sarki\" in f))" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "\"\"\"To adjust figure sizes.\"\"\"\n", - "\n", - "import matplotlib.pyplot as plt\n", - "plt.rcParams['figure.figsize'] = [20, 7]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will focus to **generation of Koma53**. Lets look the distribution of Koma53 values.\n", - "\n", - "_Ps. We can also focus KomaAE._" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "image/png": { - "height": 422, - "width": 1164 - }, - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "pd.value_counts(df_hicaz_sarki['Koma53']).plot.bar()" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Total Koma53 value: 40149\n" - ] - } - ], - "source": [ - "total_value = 0\n", - "for key, value in pd.value_counts(df_hicaz_sarki['Koma53']).items():\n", - " total_value += value;\n", - "\n", - "print (\"Total Koma53 value: \", total_value)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will map the uncommon notes into nearest common note to get rid of uncommon note problem. The reason behind that LSTM based system can not learn these uncommon notes. \n", - "\n", - "Ps. We can also discard these uncommon notes, however, we choose mapping." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "def find_nearest(array, value):\n", - " array = np.asarray(array)\n", - " idx = (np.abs(array - value)).argmin()\n", - " return (array[idx])\n", - "\n", - "def map_notes(valueCountsOfNote, threshold):\n", - " \"\"\"This function takes value counts for the Koma53-KomaAe \n", - " and gives the dict to represent which note should be \n", - " transformed into which note to get rid of uncommon\n", - " note problem.\n", - " \n", - " Arguments:\n", - " valueCountsOfNote: Value counts of Koma53 or KomaAe \n", - " threshold: What is the minimum number \n", - " to kept this note as the common note.\"\"\"\n", - " \n", - " upThresKeyList = [] # To store, which key has more value than threshold\n", - " mapDict = {} # This dict's keys store the uncommon notes and value store\n", - " # common notes.\n", - " \n", - " for key, value in valueCountsOfNote.items():\n", - " if (value > threshold):\n", - " upThresKeyList.append(key)\n", - " \n", - " upThresKeyArray = np.asarray(upThresKeyList)\n", - " \n", - " for key, value in valueCountsOfNote.items():\n", - " if key not in upThresKeyList:\n", - " nearNote = find_nearest(upThresKeyArray, key)\n", - " mapDict[key] = nearNote\n", - " return mapDict\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "mappedKoma53Dict = (map_notes(pd.value_counts(df_hicaz_sarki['Koma53']), 250))" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "df_hicaz_sarki[\"Mapped Koma53\"] = df_hicaz_sarki.Koma53.replace(to_replace=mappedKoma53Dict)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "image/png": { - "height": 422, - "width": 1164 - }, - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "pd.value_counts(df_hicaz_sarki['Mapped Koma53']).plot.bar()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, we can move into **generation** part." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "note_list = df_hicaz_sarki['Mapped Koma53'].tolist()\n", - "note_list = list(map(str, note_list))" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "save_dir = 'save' # To store trained DL model\n", - "vocab_file = os.path.join(save_dir, \"words_vocab.pkl\")" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "vocab size: 15\n" - ] - } - ], - "source": [ - "import collections\n", - "from six.moves import cPickle\n", - "\n", - "# Count the number of words\n", - "word_counts = collections.Counter(note_list)\n", - "\n", - "# Mapping from index to word : that's the vocabulary\n", - "vocabulary_inv = [x[0] for x in word_counts.most_common()]\n", - "vocabulary_inv = list(sorted(vocabulary_inv))\n", - "\n", - "# Mapping from word to index\n", - "vocab = {x: i for i, x in enumerate(vocabulary_inv)}\n", - "words = [x[0] for x in word_counts.most_common()]\n", - "\n", - "# Size of vocabulary\n", - "vocab_size = len(words)\n", - "print(\"vocab size: \", vocab_size)\n", - "\n", - "# Save the words and vocabulary as a pickle file\n", - "with open(os.path.join(vocab_file), 'wb') as f:\n", - " cPickle.dump((words, vocab, vocabulary_inv), f)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "nb sequences: 40143\n" - ] - } - ], - "source": [ - "\"\"\"We need to create two different list. One list include the previous words, \n", - "another list inclued the next word.\"\"\"\n", - "\n", - "seq_length = 6\n", - "sequences_step = 1\n", - "\n", - "sequences = []\n", - "next_words = []\n", - "for i in range(0, len(note_list) - seq_length, sequences_step):\n", - " sequences.append(note_list[i: i + seq_length])\n", - " next_words.append(note_list[i + seq_length])\n", - "\n", - "print('nb sequences:', len(sequences))" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "\"\"\"We can not use this type of array directly. So that, we have to modify this data to use with LSTM. We need \n", - "to convert into one-hot vector type array. \n", - "\n", - "List which includes previous words should have a dimension as number of sequences, number of words in sequences,\n", - "number of words in vocabulary. The other list should have a dimension as number of sequences, \n", - "number of words in vocabulary.\"\"\"\n", - "\n", - "X = np.zeros((len(sequences), seq_length, vocab_size), dtype=np.bool)\n", - "y = np.zeros((len(sequences), vocab_size), dtype=np.bool)\n", - "for i, sentence in enumerate(sequences):\n", - " for t, word in enumerate(sentence):\n", - " X[i, t, vocab[word]] = 1\n", - " y[i, vocab[next_words[i]]] = 1" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "def bidirectional_lstm_model(seq_length, vocab_size):\n", - " print('Build LSTM model.')\n", - " model = Sequential()\n", - " model.add(Bidirectional(LSTM(rnn_size, activation=\"relu\", return_sequences=True, \n", - " kernel_initializer='random_normal',\n", - " bias_initializer='random_normal'),\n", - " input_shape=(seq_length, vocab_size)))\n", - " model.add(Dropout(0.7))\n", - " model.add(Bidirectional(LSTM(rnn_size, activation=\"relu\", return_sequences=True, \n", - " kernel_initializer='random_normal',\n", - " bias_initializer='random_normal')))\n", - " model.add(Dropout(0.7))\n", - " model.add(Bidirectional(LSTM(rnn_size, activation=\"relu\",\n", - " kernel_initializer='random_normal',\n", - " bias_initializer='random_normal')))\n", - " model.add(Dropout(0.3))\n", - " model.add(Dense(vocab_size))\n", - " model.add(Activation('softmax'))\n", - " \n", - " optimizer = Adam(lr=learning_rate)\n", - " callbacks=[EarlyStopping(patience=2, monitor='val_loss')]\n", - " model.compile(loss='categorical_crossentropy', optimizer=optimizer, metrics=[categorical_accuracy])\n", - " print(\"model built!\")\n", - " return model" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Build LSTM model.\n", - "model built!\n", - "_________________________________________________________________\n", - "Layer (type) Output Shape Param # \n", - "=================================================================\n", - "bidirectional_1 (Bidirection (None, 6, 256) 147456 \n", - "_________________________________________________________________\n", - "dropout_1 (Dropout) (None, 6, 256) 0 \n", - "_________________________________________________________________\n", - "bidirectional_2 (Bidirection (None, 6, 256) 394240 \n", - "_________________________________________________________________\n", - "dropout_2 (Dropout) (None, 6, 256) 0 \n", - "_________________________________________________________________\n", - "bidirectional_3 (Bidirection (None, 256) 394240 \n", - "_________________________________________________________________\n", - "dropout_3 (Dropout) (None, 256) 0 \n", - "_________________________________________________________________\n", - "dense_1 (Dense) (None, 15) 3855 \n", - "_________________________________________________________________\n", - "activation_1 (Activation) (None, 15) 0 \n", - "=================================================================\n", - "Total params: 939,791\n", - "Trainable params: 939,791\n", - "Non-trainable params: 0\n", - "_________________________________________________________________\n" - ] - } - ], - "source": [ - "rnn_size = 128 # size of RNN\n", - "learning_rate = 0.001\n", - "\n", - "md = bidirectional_lstm_model(seq_length, vocab_size)\n", - "md.summary()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can **train** the model." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Train on 36128 samples, validate on 4015 samples\n", - "Epoch 1/10\n", - "36128/36128 [==============================] - 73s 2ms/step - loss: 1.9022 - categorical_accuracy: 0.3029 - val_loss: 1.5833 - val_categorical_accuracy: 0.4167\n", - "Epoch 2/10\n", - "36128/36128 [==============================] - 64s 2ms/step - loss: 1.6176 - categorical_accuracy: 0.4177 - val_loss: 1.4995 - val_categorical_accuracy: 0.4575\n", - "Epoch 3/10\n", - "36128/36128 [==============================] - 60s 2ms/step - loss: 1.5632 - categorical_accuracy: 0.4442 - val_loss: 1.4713 - val_categorical_accuracy: 0.4767\n", - "Epoch 4/10\n", - "36128/36128 [==============================] - 64s 2ms/step - loss: 1.5324 - categorical_accuracy: 0.4601 - val_loss: 1.4445 - val_categorical_accuracy: 0.4800\n", - "Epoch 5/10\n", - "36128/36128 [==============================] - 59s 2ms/step - loss: 1.5054 - categorical_accuracy: 0.4678 - val_loss: 1.4391 - val_categorical_accuracy: 0.4752\n", - "Epoch 6/10\n", - "36128/36128 [==============================] - 58s 2ms/step - loss: 1.4933 - categorical_accuracy: 0.4728 - val_loss: 1.4246 - val_categorical_accuracy: 0.4909\n", - "Epoch 7/10\n", - "36128/36128 [==============================] - 62s 2ms/step - loss: 1.4711 - categorical_accuracy: 0.4812 - val_loss: 1.4165 - val_categorical_accuracy: 0.4800\n", - "Epoch 8/10\n", - "36128/36128 [==============================] - 58s 2ms/step - loss: 1.4617 - categorical_accuracy: 0.4843 - val_loss: 1.4094 - val_categorical_accuracy: 0.4902\n", - "Epoch 9/10\n", - "36128/36128 [==============================] - 58s 2ms/step - loss: 1.4517 - categorical_accuracy: 0.4877 - val_loss: 1.4006 - val_categorical_accuracy: 0.4986\n", - "Epoch 10/10\n", - "36128/36128 [==============================] - 73s 2ms/step - loss: 1.4405 - categorical_accuracy: 0.4940 - val_loss: 1.3898 - val_categorical_accuracy: 0.4981\n" - ] - } - ], - "source": [ - "batch_size = 64 # minibatch size\n", - "num_epochs = 10 # number of epochs\n", - "\n", - "callbacks=[EarlyStopping(patience=4, monitor='val_loss'),\n", - " ModelCheckpoint(filepath=save_dir + \"/\" + 'my_model_gen_koma53.{epoch:02d}-{val_loss:.2f}.hdf5',\\\n", - " monitor='val_loss', verbose=0, mode='auto', period=2)]\n", - "\n", - "history = md.fit(X, y,\n", - " batch_size=batch_size,\n", - " shuffle=True,\n", - " epochs=num_epochs,\n", - " callbacks=callbacks,\n", - " validation_split=0.1)\n", - "\n", - "md.save(save_dir + \"/\" + 'my_model_generate_koma53.h5')" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "image/png": { - "height": 440, - "width": 1181 - }, - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "image/png": { - "height": 440, - "width": 1168 - }, - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# summarize history for accuracy\n", - "plt.plot(history.history['categorical_accuracy'])\n", - "plt.plot(history.history['val_categorical_accuracy'])\n", - "plt.title('model accuracy')\n", - "plt.ylabel('accuracy')\n", - "plt.xlabel('epoch')\n", - "plt.legend(['train', 'test'], loc='upper left')\n", - "plt.show()\n", - "# summarize history for loss\n", - "plt.plot(history.history['loss'])\n", - "plt.plot(history.history['val_loss'])\n", - "plt.title('model loss')\n", - "plt.ylabel('loss')\n", - "plt.xlabel('epoch')\n", - "plt.legend(['train', 'test'], loc='upper left')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "image/png": { - "height": 426, - "width": 1168 - }, - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Get training and test loss histories\n", - "training_loss = history.history['loss']\n", - "test_loss = history.history['val_loss']\n", - "\n", - "# Create count of the number of epochs\n", - "epoch_count = range(1, len(training_loss) + 1)\n", - "\n", - "# Visualize loss history\n", - "plt.plot(epoch_count, training_loss, 'r--')\n", - "plt.plot(epoch_count, test_loss, 'b-')\n", - "plt.legend(['Training Loss', 'Test Loss'])\n", - "plt.xlabel('Epoch')\n", - "plt.ylabel('Loss')\n", - "plt.show();" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loading vocabulary...\n", - "loading model...\n" - ] - } - ], - "source": [ - "save_dir = \"./save/\"\n", - "\n", - "# Load vocabulary\n", - "print(\"loading vocabulary...\")\n", - "vocab_file = os.path.join(save_dir, \"words_vocab.pkl\")\n", - "\n", - "with open(os.path.join(save_dir, 'words_vocab.pkl'), 'rb') as f:\n", - " words, vocab, vocabulary_inv = cPickle.load(f)\n", - "\n", - "vocab_size = len(words)\n", - "\n", - "# Load the model\n", - "print(\"loading model...\")\n", - "model = load_model(save_dir + \"/\" + 'my_model_generate_sentences.h5')" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "def sample(preds, temperature=1):\n", - " preds = np.asarray(preds).astype('float64')\n", - " preds = np.log(preds) / temperature\n", - " exp_preds = np.exp(preds)\n", - " preds = exp_preds / np.sum(exp_preds)\n", - " probas = np.random.multinomial(1, preds, 1)\n", - " return np.argmax(probas)" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Generating text with the following seed: \"336 305 310 358 310 327\"\n", - "\n" - ] - } - ], - "source": [ - "# Iniatate sentence\n", - "seed_sentences = \"327 336 305 310 358 310 344\"\n", - "generated = ''\n", - "sentence = []\n", - "for i in range (seq_length):\n", - " sentence.append(\"312\")\n", - "\n", - "seed = seed_sentences.split()\n", - "\n", - "for i in range(len(seed)):\n", - " sentence[seq_length-i-1]=seed[len(seed)-i-1]\n", - "\n", - "generated += ' '.join(sentence)\n", - "print('Generating text with the following seed: \"' + ' '.join(sentence) + '\"')\n", - "\n", - "print ()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now **generate** an example." - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "words_number = 30000\n", - "# Generate the text\n", - "for i in range(words_number):\n", - " #create the vector\n", - " x = np.zeros((1, seq_length, vocab_size))\n", - " for t, word in enumerate(sentence):\n", - " x[0, t, vocab[word]] = 1.\n", - " #print(x.shape)\n", - "\n", - " #calculate next word\n", - " preds = model.predict(x, verbose=0)[0]\n", - " next_index = sample(preds, 0.2)\n", - " next_word = vocabulary_inv[next_index]\n", - "\n", - " #add the next word to the text\n", - " generated += \" \" + next_word\n", - " # shift the sentence by one, and and the next word at its end\n", - " sentence = sentence[1:] + [next_word]\n", - "\n", - "# print(generated)" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "generatedNotesList = []\n", - "for singleGenerated in generated.split(\" \"):\n", - " generatedNotesList.append(int(singleGenerated))" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "np.asarray(generatedNotesList)\n", - "arr = np.asarray(generatedNotesList)\n", - "generatedDataFrame = pd.DataFrame({'Koma53':arr})" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "image/png": { - "height": 436, - "width": 1164 - }, - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "pd.value_counts(generatedDataFrame['Koma53']).plot(kind='bar', title=\"Dist. of Generated Koma53\")" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "image/png": { - "height": 436, - "width": 1164 - }, - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# pd.value_counts(df_hicaz_sarki['Mapped Koma53']).plot.bar()\n", - "pd.value_counts(df_hicaz_sarki['Mapped Koma53']).plot(kind='bar', title=\"Dist. of Original Koma53\")" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - " 336 6207\n", - " 327 5333\n", - " 349 4580\n", - " 344 4182\n", - " 358 3601\n", - " 322 2782\n", - " 310 1314\n", - " 340 1030\n", - " 305 835\n", - " 296 80\n", - " 367 58\n", - "-1 4\n", - "Name: Koma53, dtype: int64" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pd.value_counts(generatedDataFrame['Koma53'])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python [conda env:turkishMusicGen] *", - "language": "python", - "name": "conda-env-turkishMusicGen-py" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.7" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} From 6fae01fa668f4d37ddf59e05caa2e3a1e44ec0f8 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Halil=20Erdo=C4=9Fan?= Date: Sat, 2 Feb 2019 13:49:11 +0100 Subject: [PATCH 4/6] Delete spec-file.txt --- spec-file.txt | 123 -------------------------------------------------- 1 file changed, 123 deletions(-) delete mode 100644 spec-file.txt diff --git a/spec-file.txt b/spec-file.txt deleted file mode 100644 index bafc410..0000000 --- a/spec-file.txt +++ /dev/null @@ -1,123 +0,0 @@ -# This file may be used to create an environment using: -# $ conda create --name --file -# platform: osx-64 -@EXPLICIT -https://repo.anaconda.com/pkgs/main/osx-64/blas-1.0-mkl.tar.bz2 -https://conda.anaconda.org/conda-forge/osx-64/bzip2-1.0.6-1.tar.bz2 -https://conda.anaconda.org/conda-forge/osx-64/c-ares-1.15.0-h470a237_1.tar.bz2 -https://repo.anaconda.com/pkgs/main/osx-64/ca-certificates-2018.03.07-0.tar.bz2 -https://repo.anaconda.com/pkgs/main/osx-64/intel-openmp-2019.1-144.tar.bz2 -https://conda.anaconda.org/anaconda/osx-64/jpeg-9b-he5867d9_2.tar.bz2 -https://repo.anaconda.com/pkgs/main/osx-64/libcxxabi-4.0.1-hcfea43d_1.tar.bz2 -https://repo.anaconda.com/pkgs/main/osx-64/libgfortran-3.0.1-h93005f0_2.tar.bz2 -https://conda.anaconda.org/conda-forge/osx-64/libgpuarray-0.7.6-h470a237_3.tar.bz2 -https://conda.anaconda.org/anaconda/osx-64/libiconv-1.15-hdd342a3_7.tar.bz2 -https://conda.anaconda.org/anaconda/osx-64/libsodium-1.0.16-h3efe00b_0.tar.bz2 -https://conda.anaconda.org/anaconda/osx-64/pandoc-2.2.3.2-0.tar.bz2 -https://conda.anaconda.org/conda-forge/osx-64/toolchain-2.3.0-0.tar.bz2 -https://repo.anaconda.com/pkgs/main/osx-64/xz-5.2.4-h1de35cc_4.tar.bz2 -https://conda.anaconda.org/conda-forge/osx-64/yaml-0.1.7-h470a237_1.tar.bz2 -https://repo.anaconda.com/pkgs/main/osx-64/zlib-1.2.11-h1de35cc_3.tar.bz2 -https://conda.anaconda.org/conda-forge/osx-64/hdf5-1.10.4-nompi_h5598ddc_1003.tar.bz2 -https://repo.anaconda.com/pkgs/main/osx-64/libcxx-4.0.1-hcfea43d_1.tar.bz2 -https://repo.anaconda.com/pkgs/main/osx-64/libpng-1.6.35-ha441bb4_0.tar.bz2 -https://conda.anaconda.org/conda-forge/osx-64/libprotobuf-3.6.1-hd28b015_0.tar.bz2 -https://repo.anaconda.com/pkgs/main/osx-64/mkl-2019.1-144.tar.bz2 -https://repo.anaconda.com/pkgs/main/osx-64/openssl-1.1.1a-h1de35cc_0.tar.bz2 -https://repo.anaconda.com/pkgs/main/osx-64/tk-8.6.8-ha441bb4_0.tar.bz2 -https://conda.anaconda.org/conda-forge/osx-64/toolchain_c_osx-64-2.3.0-0.tar.bz2 -https://conda.anaconda.org/conda-forge/osx-64/toolchain_cxx_osx-64-2.3.0-0.tar.bz2 -https://conda.anaconda.org/anaconda/osx-64/expat-2.2.6-h0a44026_0.tar.bz2 -https://repo.anaconda.com/pkgs/main/osx-64/freetype-2.9.1-hb4e5f40_0.tar.bz2 -https://conda.anaconda.org/anaconda/osx-64/icu-58.2-h4b95b61_1.tar.bz2 -https://repo.anaconda.com/pkgs/main/osx-64/libffi-3.2.1-h475c297_4.tar.bz2 -https://repo.anaconda.com/pkgs/main/osx-64/ncurses-6.1-h0a44026_1.tar.bz2 -https://conda.anaconda.org/anaconda/osx-64/pcre-8.42-h378b8a2_0.tar.bz2 -https://conda.anaconda.org/anaconda/osx-64/zeromq-4.2.5-h0a44026_1.tar.bz2 -https://conda.anaconda.org/anaconda/osx-64/gettext-0.19.8.1-h15daf44_3.tar.bz2 -https://repo.anaconda.com/pkgs/main/osx-64/libedit-3.1.20170329-hb402a30_2.tar.bz2 -https://repo.anaconda.com/pkgs/main/osx-64/readline-7.0-h1de35cc_5.tar.bz2 -https://conda.anaconda.org/anaconda/osx-64/glib-2.56.2-hd9629dc_0.tar.bz2 -https://repo.anaconda.com/pkgs/main/osx-64/sqlite-3.25.3-ha441bb4_0.tar.bz2 -https://conda.anaconda.org/anaconda/osx-64/dbus-1.13.2-h760590f_1.tar.bz2 -https://repo.anaconda.com/pkgs/main/osx-64/python-3.6.7-haf84260_0.tar.bz2 -https://conda.anaconda.org/anaconda/osx-64/qt-5.9.6-h45cd832_2.tar.bz2 -https://conda.anaconda.org/anaconda/osx-64/appnope-0.1.0-py36hf537a9a_0.tar.bz2 -https://conda.anaconda.org/conda-forge/noarch/astor-0.7.1-py_0.tar.bz2 -https://conda.anaconda.org/anaconda/osx-64/backcall-0.1.0-py36_0.tar.bz2 -https://repo.anaconda.com/pkgs/main/osx-64/certifi-2018.11.29-py36_0.tar.bz2 -https://conda.anaconda.org/anaconda/osx-64/decorator-4.3.0-py36_0.tar.bz2 -https://conda.anaconda.org/anaconda/osx-64/entrypoints-0.2.3-py36_2.tar.bz2 -https://conda.anaconda.org/conda-forge/noarch/gast-0.2.0-py_0.tar.bz2 -https://conda.anaconda.org/anaconda/osx-64/ipython_genutils-0.2.0-py36h241746c_0.tar.bz2 -https://repo.anaconda.com/pkgs/main/osx-64/kiwisolver-1.0.1-py36h0a44026_0.tar.bz2 -https://conda.anaconda.org/conda-forge/noarch/markdown-2.6.11-py_0.tar.bz2 -https://conda.anaconda.org/conda-forge/osx-64/markupsafe-1.1.0-py36h470a237_0.tar.bz2 -https://conda.anaconda.org/anaconda/osx-64/mistune-0.8.4-py36h1de35cc_0.tar.bz2 -https://repo.anaconda.com/pkgs/main/osx-64/numpy-base-1.15.4-py36h6575580_0.tar.bz2 -https://conda.anaconda.org/anaconda/osx-64/pandocfilters-1.4.2-py36_1.tar.bz2 -https://conda.anaconda.org/anaconda/osx-64/parso-0.3.1-py36_0.tar.bz2 -https://conda.anaconda.org/anaconda/osx-64/pickleshare-0.7.5-py36_0.tar.bz2 -https://conda.anaconda.org/anaconda/osx-64/prometheus_client-0.4.2-py36_0.tar.bz2 -https://conda.anaconda.org/anaconda/osx-64/ptyprocess-0.6.0-py36_0.tar.bz2 -https://repo.anaconda.com/pkgs/main/osx-64/pyparsing-2.3.0-py36_0.tar.bz2 -https://repo.anaconda.com/pkgs/main/osx-64/pytz-2018.7-py36_0.tar.bz2 -https://conda.anaconda.org/conda-forge/osx-64/pyyaml-3.13-py36h470a237_1.tar.bz2 -https://conda.anaconda.org/anaconda/osx-64/pyzmq-17.1.2-py36h1de35cc_0.tar.bz2 -https://conda.anaconda.org/anaconda/osx-64/send2trash-1.5.0-py36_0.tar.bz2 -https://conda.anaconda.org/anaconda/osx-64/sip-4.19.13-py36h0a44026_0.tar.bz2 -https://repo.anaconda.com/pkgs/main/osx-64/six-1.12.0-py36_0.tar.bz2 -https://conda.anaconda.org/conda-forge/noarch/termcolor-1.1.0-py_2.tar.bz2 -https://conda.anaconda.org/anaconda/osx-64/testpath-0.4.2-py36_0.tar.bz2 -https://repo.anaconda.com/pkgs/main/osx-64/tornado-5.1.1-py36h1de35cc_0.tar.bz2 -https://conda.anaconda.org/anaconda/osx-64/wcwidth-0.1.7-py36h8c6ec74_0.tar.bz2 -https://conda.anaconda.org/anaconda/osx-64/webencodings-0.5.1-py36_1.tar.bz2 -https://conda.anaconda.org/conda-forge/noarch/werkzeug-0.14.1-py_0.tar.bz2 -https://conda.anaconda.org/conda-forge/osx-64/absl-py-0.6.1-py36_1000.tar.bz2 -https://repo.anaconda.com/pkgs/main/osx-64/cycler-0.10.0-py36hfc81398_0.tar.bz2 -https://conda.anaconda.org/anaconda/osx-64/jedi-0.13.1-py36_0.tar.bz2 -https://conda.anaconda.org/conda-forge/noarch/mako-1.0.7-py_1.tar.bz2 -https://repo.anaconda.com/pkgs/main/osx-64/mkl_fft-1.0.6-py36h27c97d8_0.tar.bz2 -https://repo.anaconda.com/pkgs/main/osx-64/mkl_random-1.0.2-py36h27c97d8_0.tar.bz2 -https://conda.anaconda.org/anaconda/osx-64/pexpect-4.6.0-py36_0.tar.bz2 -https://conda.anaconda.org/anaconda/osx-64/pyqt-5.9.2-py36h655552a_0.tar.bz2 -https://repo.anaconda.com/pkgs/main/osx-64/python-dateutil-2.7.5-py36_0.tar.bz2 -https://repo.anaconda.com/pkgs/main/osx-64/setuptools-40.6.2-py36_0.tar.bz2 -https://conda.anaconda.org/anaconda/osx-64/terminado-0.8.1-py36_1.tar.bz2 -https://conda.anaconda.org/anaconda/osx-64/traitlets-4.3.2-py36h65bd3ce_0.tar.bz2 -https://conda.anaconda.org/anaconda/osx-64/bleach-3.0.2-py36_0.tar.bz2 -https://repo.anaconda.com/pkgs/main/osx-64/grpcio-1.16.1-py36h044775b_1.tar.bz2 -https://conda.anaconda.org/anaconda/osx-64/jinja2-2.10-py36_0.tar.bz2 -https://conda.anaconda.org/anaconda/osx-64/jsonschema-2.6.0-py36hb385e00_0.tar.bz2 -https://conda.anaconda.org/anaconda/osx-64/jupyter_core-4.4.0-py36_0.tar.bz2 -https://repo.anaconda.com/pkgs/main/osx-64/numpy-1.15.4-py36hacdab7b_0.tar.bz2 -https://conda.anaconda.org/conda-forge/osx-64/protobuf-3.6.1-py36hfc679d8_1.tar.bz2 -https://conda.anaconda.org/anaconda/osx-64/pygments-2.2.0-py36h240cd3f_0.tar.bz2 -https://repo.anaconda.com/pkgs/main/osx-64/wheel-0.32.3-py36_0.tar.bz2 -https://conda.anaconda.org/conda-forge/osx-64/h5py-2.8.0-py36he5c79e1_5.tar.bz2 -https://conda.anaconda.org/anaconda/osx-64/jupyter_client-5.2.3-py36_0.tar.bz2 -https://repo.anaconda.com/pkgs/main/osx-64/matplotlib-3.0.2-py36h54f8f79_0.tar.bz2 -https://conda.anaconda.org/anaconda/osx-64/nbformat-4.4.0-py36h827af21_0.tar.bz2 -https://repo.anaconda.com/pkgs/main/osx-64/pandas-0.23.4-py36h6440ff4_0.tar.bz2 -https://repo.anaconda.com/pkgs/main/osx-64/pip-18.1-py36_0.tar.bz2 -https://conda.anaconda.org/anaconda/osx-64/prompt_toolkit-2.0.7-py36_0.tar.bz2 -https://conda.anaconda.org/conda-forge/osx-64/pygpu-0.7.6-py36h7eb728f_0.tar.bz2 -https://repo.anaconda.com/pkgs/main/osx-64/scipy-1.1.0-py36h1410ff5_2.tar.bz2 -https://conda.anaconda.org/conda-forge/osx-64/tensorboard-1.10.0-py36_0.tar.bz2 -https://conda.anaconda.org/anaconda/osx-64/ipython-7.2.0-py36h39e3cac_0.tar.bz2 -https://conda.anaconda.org/jupycon/label/dev/osx-64/nb_conda_kernels-2.2.0+5.gdb8c10d-py36_0.tar.bz2 -https://conda.anaconda.org/anaconda/osx-64/nbconvert-5.3.1-py36_0.tar.bz2 -https://conda.anaconda.org/conda-forge/osx-64/tensorflow-1.10.0-py36_0.tar.bz2 -https://conda.anaconda.org/conda-forge/osx-64/theano-1.0.3-py36hfc679d8_1.tar.bz2 -https://conda.anaconda.org/anaconda/osx-64/ipykernel-5.1.0-py36h39e3cac_0.tar.bz2 -https://conda.anaconda.org/anaconda/osx-64/jupyter_console-6.0.0-py36_0.tar.bz2 -https://conda.anaconda.org/anaconda/osx-64/notebook-5.7.2-py36_0.tar.bz2 -https://conda.anaconda.org/anaconda/osx-64/qtconsole-4.4.2-py36_0.tar.bz2 -https://conda.anaconda.org/conda-forge/noarch/jupyterlab_server-0.2.0-py_0.tar.bz2 -https://conda.anaconda.org/anaconda/osx-64/widgetsnbextension-3.4.2-py36_0.tar.bz2 -https://conda.anaconda.org/anaconda/osx-64/ipywidgets-7.4.2-py36_0.tar.bz2 -https://conda.anaconda.org/conda-forge/osx-64/jupyterlab-0.35.4-py36_0.tar.bz2 -https://conda.anaconda.org/anaconda/osx-64/jupyter-1.0.0-py36_7.tar.bz2 -https://conda.anaconda.org/conda-forge/noarch/keras-applications-1.0.4-py_1.tar.bz2 -https://conda.anaconda.org/conda-forge/osx-64/keras-2.2.4-py36_0.tar.bz2 -https://conda.anaconda.org/conda-forge/noarch/keras-preprocessing-1.0.2-py_1.tar.bz2 From 43806711788b9a3b314bdc2beb15a6edca2d3690 Mon Sep 17 00:00:00 2001 From: hedonistrh Date: Sat, 2 Feb 2019 14:18:35 +0100 Subject: [PATCH 5/6] updated environment.yml file for mock training has been added. --- environment.yml | 131 ++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 131 insertions(+) create mode 100644 environment.yml diff --git a/environment.yml b/environment.yml new file mode 100644 index 0000000..7907f93 --- /dev/null +++ b/environment.yml @@ -0,0 +1,131 @@ +name: turkishMusicGen +channels: + - jupycon/label/dev + - conda-forge + - anaconda + - defaults +dependencies: + - appnope=0.1.0=py36hf537a9a_0 + - backcall=0.1.0=py36_0 + - bleach=3.0.2=py36_0 + - dbus=1.13.2=h760590f_1 + - decorator=4.3.0=py36_0 + - entrypoints=0.2.3=py36_2 + - expat=2.2.6=h0a44026_0 + - gettext=0.19.8.1=h15daf44_3 + - glib=2.56.2=hd9629dc_0 + - icu=58.2=h4b95b61_1 + - ipykernel=5.1.0=py36h39e3cac_0 + - ipython=7.2.0=py36h39e3cac_0 + - ipython_genutils=0.2.0=py36h241746c_0 + - ipywidgets=7.4.2=py36_0 + - jedi=0.13.1=py36_0 + - jinja2=2.10=py36_0 + - jpeg=9b=he5867d9_2 + - jsonschema=2.6.0=py36hb385e00_0 + - jupyter=1.0.0=py36_7 + - jupyter_client=5.2.3=py36_0 + - jupyter_console=6.0.0=py36_0 + - jupyter_core=4.4.0=py36_0 + - libiconv=1.15=hdd342a3_7 + - libsodium=1.0.16=h3efe00b_0 + - mistune=0.8.4=py36h1de35cc_0 + - nbconvert=5.3.1=py36_0 + - nbformat=4.4.0=py36h827af21_0 + - notebook=5.7.2=py36_0 + - pandoc=2.2.3.2=0 + - pandocfilters=1.4.2=py36_1 + - parso=0.3.1=py36_0 + - pcre=8.42=h378b8a2_0 + - pexpect=4.6.0=py36_0 + - pickleshare=0.7.5=py36_0 + - prometheus_client=0.4.2=py36_0 + - prompt_toolkit=2.0.7=py36_0 + - ptyprocess=0.6.0=py36_0 + - pygments=2.2.0=py36h240cd3f_0 + - pyqt=5.9.2=py36h655552a_0 + - pyzmq=17.1.2=py36h1de35cc_0 + - qt=5.9.6=h45cd832_2 + - qtconsole=4.4.2=py36_0 + - send2trash=1.5.0=py36_0 + - sip=4.19.13=py36h0a44026_0 + - terminado=0.8.1=py36_1 + - testpath=0.4.2=py36_0 + - traitlets=4.3.2=py36h65bd3ce_0 + - wcwidth=0.1.7=py36h8c6ec74_0 + - webencodings=0.5.1=py36_1 + - widgetsnbextension=3.4.2=py36_0 + - zeromq=4.2.5=h0a44026_1 + - absl-py=0.6.1=py36_1000 + - astor=0.7.1=py_0 + - bzip2=1.0.6=1 + - c-ares=1.15.0=h470a237_1 + - gast=0.2.0=py_0 + - h5py=2.8.0=py36he5c79e1_5 + - hdf5=1.10.4=nompi_h5598ddc_1003 + - jupyterlab=0.35.4=py36_0 + - jupyterlab_server=0.2.0=py_0 + - keras=2.2.4=py36_0 + - keras-applications=1.0.4=py_1 + - keras-preprocessing=1.0.2=py_1 + - libgpuarray=0.7.6=h470a237_3 + - libprotobuf=3.6.1=hd28b015_0 + - mako=1.0.7=py_1 + - markdown=2.6.11=py_0 + - markupsafe=1.1.0=py36h470a237_0 + - protobuf=3.6.1=py36hfc679d8_1 + - pygpu=0.7.6=py36h7eb728f_0 + - pyyaml=3.13=py36h470a237_1 + - tensorboard=1.10.0=py36_0 + - tensorflow=1.10.0=py36_0 + - termcolor=1.1.0=py_2 + - theano=1.0.3=py36hfc679d8_1 + - toolchain=2.3.0=0 + - toolchain_c_osx-64=2.3.0=0 + - toolchain_cxx_osx-64=2.3.0=0 + - werkzeug=0.14.1=py_0 + - yaml=0.1.7=h470a237_1 + - blas=1.0=mkl + - ca-certificates=2018.03.07=0 + - certifi=2018.11.29=py36_0 + - cycler=0.10.0=py36hfc81398_0 + - freetype=2.9.1=hb4e5f40_0 + - grpcio=1.16.1=py36h044775b_1 + - intel-openmp=2019.1=144 + - kiwisolver=1.0.1=py36h0a44026_0 + - libcxx=4.0.1=hcfea43d_1 + - libcxxabi=4.0.1=hcfea43d_1 + - libedit=3.1.20170329=hb402a30_2 + - libffi=3.2.1=h475c297_4 + - libgfortran=3.0.1=h93005f0_2 + - libpng=1.6.35=ha441bb4_0 + - matplotlib=3.0.2=py36h54f8f79_0 + - mkl=2019.1=144 + - mkl_fft=1.0.6=py36h27c97d8_0 + - mkl_random=1.0.2=py36h27c97d8_0 + - ncurses=6.1=h0a44026_1 + - numpy=1.15.4=py36hacdab7b_0 + - numpy-base=1.15.4=py36h6575580_0 + - openssl=1.1.1a=h1de35cc_0 + - pandas=0.23.4=py36h6440ff4_0 + - pip=18.1=py36_0 + - pyparsing=2.3.0=py36_0 + - python=3.6.7=haf84260_0 + - python-dateutil=2.7.5=py36_0 + - pytz=2018.7=py36_0 + - readline=7.0=h1de35cc_5 + - scipy=1.1.0=py36h1410ff5_2 + - setuptools=40.6.2=py36_0 + - six=1.12.0=py36_0 + - sqlite=3.25.3=ha441bb4_0 + - tk=8.6.8=ha441bb4_0 + - tornado=5.1.1=py36h1de35cc_0 + - wheel=0.32.3=py36_0 + - xz=5.2.4=h1de35cc_4 + - zlib=1.2.11=h1de35cc_3 + - nb_conda_kernels=2.2.0+5.gdb8c10d=py36_0 + - pip: + - mccabe==0.6.1 + - pycodestyle==2.5.0 +prefix: /Users/herdogan/anaconda3/envs/turkishMusicGen + From 02dda380c3ddce42ccdb304aaf69123a13dab83e Mon Sep 17 00:00:00 2001 From: hedonistrh Date: Sat, 2 Feb 2019 14:18:52 +0100 Subject: [PATCH 6/6] Code has been changed according to PEP-8. --- MockTraining.ipynb | 830 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 830 insertions(+) create mode 100644 MockTraining.ipynb diff --git a/MockTraining.ipynb b/MockTraining.ipynb new file mode 100644 index 0000000..fcc1f23 --- /dev/null +++ b/MockTraining.ipynb @@ -0,0 +1,830 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Mock training for the baseline system which is LSTM. For the dataset, we will use hicaz--sarki datas." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Using TensorFlow backend.\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import os\n", + "import numpy as np\n", + "from keras.models import Sequential, Model\n", + "from keras.layers import Dense, Activation, Dropout, GRU\n", + "from keras.layers import LSTM, Input, Bidirectional\n", + "from keras.optimizers import Adam\n", + "from keras.callbacks import EarlyStopping\n", + "from keras.callbacks import ModelCheckpoint\n", + "from keras.metrics import categorical_accuracy\n", + "from keras.layers.embeddings import Embedding\n", + "from keras.layers.recurrent import SimpleRNN\n", + "from keras.layers.wrappers import TimeDistributed\n", + "from keras.layers import Convolution1D\n", + "from keras.models import load_model\n", + "%matplotlib inline \n", + "%config InlineBackend.figure_format = 'retina' # for Mac\n", + "import matplotlib.pyplot as plt\n", + "plt.rcParams['figure.figsize'] = [20, 7]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Download [datas](https://minhaskamal.github.io/DownGit/#/home?url=https://github.com/MTG/SymbTr/tree/master/txt) (which command will download 1000 .txt file from [SymbTR](https://github.com/MTG/SymbTr/tree/master/txt)) and extract from zip. After that, you can follow this notebook." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Upload the datas into panda's dataframe. As I mentioned before, for mock training, we have just used hicaz--sarki datas." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import glob\n", + "root_dir = \"./txt/\"\n", + "root_dir = glob.glob(os.path.join(root_dir, \"*txt\"))\n", + "\n", + "df_hicaz_sarki = pd.concat((pd.read_csv(f, sep=\"\\t\") for f in root_dir if \"hicaz--sarki\" in f))" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"To adjust figure sizes.\"\"\"\n", + "\n", + "import matplotlib.pyplot as plt\n", + "plt.rcParams['figure.figsize'] = [20, 7]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will focus to **generation of Koma53**. Lets look the distribution of Koma53 values.\n", + "\n", + "_Ps. We can also focus KomaAE._" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 422, + "width": 1164 + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "pd.value_counts(df_hicaz_sarki['Koma53']).plot.bar()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total Koma53 value: 40149\n" + ] + } + ], + "source": [ + "total_value = 0\n", + "for key, value in pd.value_counts(df_hicaz_sarki['Koma53']).items():\n", + " total_value += value;\n", + "\n", + "print (\"Total Koma53 value: \", total_value)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will map the uncommon notes into nearest common note to get rid of uncommon note problem. The reason behind that LSTM based system can not learn these uncommon notes. \n", + "\n", + "Ps. We can also discard these uncommon notes, however, we choose mapping." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "def find_nearest(array, value):\n", + " array = np.asarray(array)\n", + " idx = (np.abs(array - value)).argmin()\n", + " return (array[idx])\n", + "\n", + "def map_notes(value_counts_of_note, threshold):\n", + " \"\"\"This function takes value counts for the Koma53-KomaAe \n", + " and gives the dict to represent which note should be \n", + " transformed into which note to get rid of uncommon\n", + " note problem.\n", + " \n", + " Arguments:\n", + " valueCountsOfNote: Value counts of Koma53 or KomaAe \n", + " threshold: What is the minimum number \n", + " to kept this note as the common note.\"\"\"\n", + " \n", + " up_thres_key_list = [] # To store, which key has more value than threshold\n", + " map_dict = {} # This dict's keys store the uncommon notes and value store\n", + " # common notes.\n", + " \n", + " for key, value in value_counts_of_note.items():\n", + " if (value > threshold):\n", + " up_thres_key_list.append(key)\n", + " \n", + " up_thres_key_array = np.asarray(up_thres_key_list)\n", + " \n", + " for key, value in value_counts_of_note.items():\n", + " if key not in up_thres_key_list:\n", + " near_note = find_nearest(up_thres_key_array, key)\n", + " map_dict[key] = near_note\n", + " return map_dict\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "mapped_koma53_dict = (map_notes(pd.value_counts(df_hicaz_sarki['Koma53']), 250))" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "df_hicaz_sarki[\"Mapped Koma53\"] = df_hicaz_sarki.Koma53.replace(to_replace=mapped_koma53_dict)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 422, + "width": 1164 + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "pd.value_counts(df_hicaz_sarki['Mapped Koma53']).plot.bar()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we can move into **generation** part." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "note_list = df_hicaz_sarki['Mapped Koma53'].tolist()\n", + "note_list = list(map(str, note_list))" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "save_dir = 'save' # To store trained DL model\n", + "vocab_file = os.path.join(save_dir, \"words_vocab.pkl\")" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "vocab size: 15\n" + ] + } + ], + "source": [ + "import collections\n", + "from six.moves import cPickle\n", + "\n", + "# Count the number of words\n", + "word_counts = collections.Counter(note_list)\n", + "\n", + "# Mapping from index to word : that's the vocabulary\n", + "vocabulary_inv = [x[0] for x in word_counts.most_common()]\n", + "vocabulary_inv = list(sorted(vocabulary_inv))\n", + "\n", + "# Mapping from word to index\n", + "vocab = {x: i for i, x in enumerate(vocabulary_inv)}\n", + "words = [x[0] for x in word_counts.most_common()]\n", + "\n", + "# Size of vocabulary\n", + "vocab_size = len(words)\n", + "print(\"vocab size: \", vocab_size)\n", + "\n", + "# Save the words and vocabulary as a pickle file\n", + "with open(os.path.join(vocab_file), 'wb') as f:\n", + " cPickle.dump((words, vocab, vocabulary_inv), f)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "nb sequences: 40143\n" + ] + } + ], + "source": [ + "\"\"\"We need to create two different list. One list include the previous words, \n", + "another list inclued the next word.\"\"\"\n", + "\n", + "seq_length = 6\n", + "sequences_step = 1\n", + "\n", + "sequences = []\n", + "next_words = []\n", + "for i in range(0, len(note_list) - seq_length, sequences_step):\n", + " sequences.append(note_list[i: i + seq_length])\n", + " next_words.append(note_list[i + seq_length])\n", + "\n", + "print('nb sequences:', len(sequences))" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"We can not use this type of array directly. So that, we have to modify this data to use with LSTM. We need \n", + "to convert into one-hot vector type array. \n", + "\n", + "List which includes previous words should have a dimension as number of sequences, number of words in sequences,\n", + "number of words in vocabulary. The other list should have a dimension as number of sequences, \n", + "number of words in vocabulary.\"\"\"\n", + "\n", + "X = np.zeros((len(sequences), seq_length, vocab_size), dtype=np.bool)\n", + "y = np.zeros((len(sequences), vocab_size), dtype=np.bool)\n", + "for i, sentence in enumerate(sequences):\n", + " for t, word in enumerate(sentence):\n", + " X[i, t, vocab[word]] = 1\n", + " y[i, vocab[next_words[i]]] = 1" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "def bidirectional_lstm_model(seq_length, vocab_size):\n", + " print('Build LSTM model.')\n", + " model = Sequential()\n", + " model.add(Bidirectional(LSTM(rnn_size, activation=\"relu\", return_sequences=True, \n", + " kernel_initializer='random_normal',\n", + " bias_initializer='random_normal'),\n", + " input_shape=(seq_length, vocab_size)))\n", + " model.add(Dropout(0.7))\n", + " model.add(Bidirectional(LSTM(rnn_size, activation=\"relu\", return_sequences=True, \n", + " kernel_initializer='random_normal',\n", + " bias_initializer='random_normal')))\n", + " model.add(Dropout(0.7))\n", + " model.add(Bidirectional(LSTM(rnn_size, activation=\"relu\",\n", + " kernel_initializer='random_normal',\n", + " bias_initializer='random_normal')))\n", + " model.add(Dropout(0.3))\n", + " model.add(Dense(vocab_size))\n", + " model.add(Activation('softmax'))\n", + " \n", + " optimizer = Adam(lr=learning_rate)\n", + " callbacks=[EarlyStopping(patience=2, monitor='val_loss')]\n", + " model.compile(loss='categorical_crossentropy', optimizer=optimizer, metrics=[categorical_accuracy])\n", + " print(\"model built!\")\n", + " return model" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Build LSTM model.\n", + "model built!\n", + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "bidirectional_1 (Bidirection (None, 6, 256) 147456 \n", + "_________________________________________________________________\n", + "dropout_1 (Dropout) (None, 6, 256) 0 \n", + "_________________________________________________________________\n", + "bidirectional_2 (Bidirection (None, 6, 256) 394240 \n", + "_________________________________________________________________\n", + "dropout_2 (Dropout) (None, 6, 256) 0 \n", + "_________________________________________________________________\n", + "bidirectional_3 (Bidirection (None, 256) 394240 \n", + "_________________________________________________________________\n", + "dropout_3 (Dropout) (None, 256) 0 \n", + "_________________________________________________________________\n", + "dense_1 (Dense) (None, 15) 3855 \n", + "_________________________________________________________________\n", + "activation_1 (Activation) (None, 15) 0 \n", + "=================================================================\n", + "Total params: 939,791\n", + "Trainable params: 939,791\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "rnn_size = 128 # size of RNN\n", + "learning_rate = 0.001\n", + "\n", + "md = bidirectional_lstm_model(seq_length, vocab_size)\n", + "md.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can **train** the model." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train on 36128 samples, validate on 4015 samples\n", + "Epoch 1/10\n", + "36128/36128 [==============================] - 68s 2ms/step - loss: 1.8946 - categorical_accuracy: 0.3036 - val_loss: 1.5945 - val_categorical_accuracy: 0.4152\n", + "Epoch 2/10\n", + "36128/36128 [==============================] - 56s 2ms/step - loss: 1.6182 - categorical_accuracy: 0.4157 - val_loss: 1.5122 - val_categorical_accuracy: 0.4413\n", + "Epoch 3/10\n", + "36128/36128 [==============================] - 57s 2ms/step - loss: 1.5611 - categorical_accuracy: 0.4397 - val_loss: 1.4855 - val_categorical_accuracy: 0.4665\n", + "Epoch 4/10\n", + "36128/36128 [==============================] - 61s 2ms/step - loss: 1.5310 - categorical_accuracy: 0.4526 - val_loss: 1.4494 - val_categorical_accuracy: 0.4755\n", + "Epoch 5/10\n", + "36128/36128 [==============================] - 52s 1ms/step - loss: 1.5068 - categorical_accuracy: 0.4657 - val_loss: 1.4261 - val_categorical_accuracy: 0.4802\n", + "Epoch 6/10\n", + "36128/36128 [==============================] - 51s 1ms/step - loss: 1.4880 - categorical_accuracy: 0.4724 - val_loss: 1.4231 - val_categorical_accuracy: 0.4879\n", + "Epoch 7/10\n", + "36128/36128 [==============================] - 83s 2ms/step - loss: 1.4716 - categorical_accuracy: 0.4774 - val_loss: 1.4214 - val_categorical_accuracy: 0.4924\n", + "Epoch 8/10\n", + "36128/36128 [==============================] - 61s 2ms/step - loss: 1.4606 - categorical_accuracy: 0.4841 - val_loss: 1.4107 - val_categorical_accuracy: 0.4879\n", + "Epoch 9/10\n", + "36128/36128 [==============================] - 64s 2ms/step - loss: 1.4466 - categorical_accuracy: 0.4886 - val_loss: 1.4027 - val_categorical_accuracy: 0.4877\n", + "Epoch 10/10\n", + "36128/36128 [==============================] - 56s 2ms/step - loss: 1.4394 - categorical_accuracy: 0.4924 - val_loss: 1.3978 - val_categorical_accuracy: 0.4976\n" + ] + } + ], + "source": [ + "batch_size = 64 # minibatch size\n", + "num_epochs = 10 # number of epochs\n", + "\n", + "callbacks=[EarlyStopping(patience=4, monitor='val_loss'),\n", + " ModelCheckpoint(filepath=save_dir + \"/\" + 'my_model_gen_koma53.{epoch:02d}-{val_loss:.2f}.hdf5',\\\n", + " monitor='val_loss', verbose=0, mode='auto', period=2)]\n", + "\n", + "history = md.fit(X, y,\n", + " batch_size=batch_size,\n", + " shuffle=True,\n", + " epochs=num_epochs,\n", + " callbacks=callbacks,\n", + " validation_split=0.1)\n", + "\n", + "md.save(save_dir + \"/\" + 'my_model_generate_koma53.h5')" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAACToAAANwCAYAAAAF3Uc6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAWJQAAFiUBSVIk8AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAIABJREFUeJzs3XucZHV95//3py5dfa2+zjAMM8wFZhAwOoICgqsDGDUSkVUMKyZh1BV/2cQEdHfVKMuMD7zw0+gOPn6urq5A1EXjbIKJKBAjgygkrLBRdBEYcJhhmHvfu7ru398f51TXqeqq7q6u6vvr+Xicxzl1zvme8z3VPUPT9Z7Px5xzAgAAAAAAAAAAAAAAAIDFLLTQEwAAAAAAAAAAAAAAAACA6RB0AgAAAAAAAAAAAAAAALDoEXQCAAAAAAAAAAAAAAAAsOgRdAIAAAAAAAAAAAAAAACw6BF0AgAAAAAAAAAAAAAAALDoEXQCAAAAAAAAAAAAAAAAsOgRdAIAAAAAAAAAAAAAAACw6BF0AgAAAAAAAAAAAAAAALDoEXQCAAAAAAAAAAAAAAAAsOgRdAIAAAAAAAAAAAAAAACw6BF0AgAAAAAAAAAAAAAAALDoEXQCAAAAAAAAAAAAAAAAsOgRdAIAAAAAAAAAAAAAAACw6BF0AgAAAAAAwJwxM+cvGxt4zb3+NXc06poAAAAAAABY/Ag6AQAAAAAAAAAAAAAAAFj0CDoBAAAAAAAAAAAAAAAAWPQIOgEAAAAAAAAAAAAAAABY9Ag6AQAAAAAAAAAAAAAAAFj0CDoBAAAAAAAscma238ycmW03s1PN7MtmdtDMxs3sSTO70cxCgfPfYWYPmdmgmQ2b2T1m9tJp7vEKM/umf92UmZ0ws/vM7O3TjAuZ2QfM7Bf+fI6b2T+Y2atn+GyrzOzTZvaEmY2a2ZiZ/crMPmlmPTN7h2bGzJrM7Aoz+6o/3xNmljSz583sW2Z2/gyucbb//j/tz3XQn/tt1cabWa+Z7TKzx/zzE/74b5vZW8vO3el/re+YYg53+OfsLNu/3d+/33/9e2b2QzM7ZmZ5M7shcO6F/vv+z2Z2yMzS/nn3mtnVM3gfZvRMZvZ1f057prneLv+8h6e7NwAAAAAAWLkiCz0BAAAAAAAAzNgmSXdJWiNpWFJU0kskfV7SZkkfMLPPSPqwpJykhKQOSW+WdLGZXeCce6b8omZ2vaT/puI/ihuU1CXpDZLeYGbflLTDOZcrGxeRtEdSIdiSlff7pt+X9CYzu2aqhzGz10j6nqRCoCntz/tcf/kjM/td59xT0781M/IGSf8QeJ2Q5CSdLulaSX9gZu9xzn2jynw/IOkLksL+rjFJTZJe6i8vk7S9bMy/kfR3knr9XWlJSUlb/OUaSVbnc1Wa64ckfU7e8w1JygeOtUv658DpGX9OqyS9UdIbzey/O+feX+XatTzT1yS9W9JbzKzXOXeywvVM0nX+y6/X/LAAAAAAAGDFoKITAAAAAADA0vEFSb+V9HLnXKekuKSb/GN/amZ/KemDkm6Q1Omci0v6HUlPyQsufbL8gmZ2sYohpz2S1jvnuv3zPyYvKPOHkj5aYT4flhdyykv6T/49u+WFrn6kKUIrZrZBXuioR14Y5iWSWiS1yQsN3StpvaS/NbNwtevUaFTS7ZIul9TnnGtzzrVI2iDpv8oLaf13Mzu9wnzfIek2eSGnPZLOcc61+/NdK+89eqxszBmSvi8vEPSvki6T1Op/7XrkhYr+tkHPFnSKpFslfUnSqf7XpN2ft+R9vX4g6Z2STpPU7H+vdEv6gLz36Xr/mUvU+kzOuYcl/V95gbB3VZnv5fK+BmOSvjPrpwYAAAAAAMueOecWeg4AAAAAAACYgt+KbIOkAUmbnXODZcf/SV7gRJJuds59ouz4v5H0E0kpSXHnXLrC2J9Jel2Fqk2fkhdyGpV0mnNu2N/fJulFeWGrXc65nWXjYpIel3SOv2uTc25/4Pg35QVfbnPO/UWFZ26S9Kikl0t6h3NuT+DYXkmvk/Ru59wd5WNny8z+h6T3SNrpnNsV2B+V9JykdZLucs5dO8Pr/Y2kd0h6WtIrnXMjMxizU9LNku50zu2ocs4d8ioglbzvZrZd0gP+yxnPs8L1/0jSX0va65y7tOzYbJ7pRnlVx/7VOfeKCsf/p7zQVdVnBgAAAAAAkKjoBAAAAAAAsJR8uTzk5PuRv07LC5SU+5m81mIxSWcWdppZj6RCkOXT5SEn363+2HZ5LfAK3iAv5JSSV2mqhHMuJa912iRm1iIvLKMq85UfxiqEm3630jlzoNDW7pKy/ZfLCznl5FWumpbfHu7f+i//y0wCQQ322TrGFt6Hi4LVtOp4pr+W9725zcxKgk5m1hm4Jm3rAAAAAADAlAg6AQAAAAAALB1PVNl/zF/vd86Nlh90zuUlnfBfdgcOvUKSyWtP92ClCzvnhlRsyXZe4FBh+1/9cyqpeE1Jr5TXykyS/sXMjlRaVAwVra9ynZqZWY+Z3WRmD5vZSTPLmpkzMyfp7/zT1pYNu8hf/8I5d2iGt3qlvFZ4Tl4bvvk0LukXU51gZhEze6+Z3Wtmh80sFXgfBvzTmlX6/TKrZ3LOnZR0t//y3WWHr/Xv84xz7iczvSYAAAAAAFiZIgs9AQAAAAAAAMzY4Sr7c9McD54TDexb5a+HKgWkAl4oOz+4/eIU46qFgk4NbJ8yxfiC1hmcMy0zO0fSj8vuOSIvGOTkha+6JbWVDS2cf6CG2xXGDE0RBJsrJ/1wW0V+Zab7JF0c2D0u6bikwrjC/NtUDMnV80xfk/QHkq41s/8YaJ/4Hn99e43XAwAAAAAAKxAVnQAAAAAAABCb5/sVfic14JyzGSzbG3Tf2+WFdR6X9CZJHc65uHPuFOfcGhXb6VnZuPLXMzGbMY1SqQVh0E3yQk4nJF0n6RTnXKtzbrX/PpwWONeqbNfqR5J+K6lX0pWSZGbnyqsSlZN0Zx3XBgAAAAAAKwRBJwAAAAAAgJXruL9uMbNVU5y3ruz84HZ5m7egaseO+utuM1sz9RQbw8xOl3SBvFDNlc65+ypUsapWXeqIv95Qwy0LYzrNrLOGcVl/3TzFObVcr5JCoOsDzrm/ds4dKzs+3ftQ6zPJOeckfd1/WWhf915/fZ9zbqrKYAAAAAAAAJIIOgEAAAAAAKxk/0deyzZJurTSCX6g5Xz/5eOBQ4XtbWYWr3L911XZ/3MVAz1vm9lU6zYR1nLOVWup9/oq+//ZX7/MzE6rck65wjOapN+b4RhJGvTX6yodNDNT8esxW4Vr/58qx6u9D7N9poLb5QXN3mhmGyT9ob//69WHAAAAAAAAFBF0AgAAAAAAWKGcc/2SHvBfftjMKv2u6MPyqguNSvpBYP99kobltb37i/JBZtYk6UNV7jsi6X/5Lz9uZtUqCMnMImbWPs2jzMSQvz7FzFZXuM/vSLq2yth/knRIUljSZ2dyM79a1N/5L3eZWccM5/mEv36VmZ1a4fi7JK2f4bWqKbwXv1N+wH+vP1ZpUB3PVBh/SNIP5b2P35K0Sl5lsL+v5ToAAAAAAGDlIugEAAAAAACwst0kKS/pPEnfNrN1khd4MbO/lPQR/7zPOOeGC4OccwlJ/6//8mYz+6CZtfhjN8oLxEwVyPmIpH5Jp0p62Mz+rZnFCgfN7Ewzu0HSk5JeWfdTetd5QV41ou+Y2Zn+faJm9jZJ/ygvzDWJcy6jYmjrnWb2N2b2ksBcTzWz95nZbWVD/1LSiKStkn5iZpcWwmRm1mVmV5jZPWVjfibpRUlNku4ys03++a1m9n5JX5U0MMv3oOAf/fXnzex1fpUomdmr5IW6+qYYO5tnCvqav77EX3/Tf38BAAAAAACmFVnoCQAAAAAAAGDhOOceNrP/IOlLkt4h6WozG5QUl1d5R/Kq73ymwvBbJb1K0lsl/ZWkW81sVFKXvBZn16hYuan8vvvN7E2S7pa0WdLfSsqa2ZCkdnmVoiZOr+shvfvlzezPJe2RtF3SM2Y24t+nSdIBSf9R0jeqjP+O37bus/Lep3f4zxqW1OKf9mDZmH1m9lb/2bZJ+rGklJklJXVWuU/WzP7Mn+frJD1nZsOSWuX9Lu/r/j2vm8374Pu4pN+VF0TbKylpZjlJbZLGJV0lr2JXpfnV/Exl7pF0WF7ATaJtHQAAAAAAqAEVnQAAAAAAAFY459xX5AWW/qe8EEq7vPZm/yjpHc65P3TO5SqMy0p6u6Q/l/RLeeGmnLwwy+ucc387zX3/t6SXyGuP97C8SkFd8sI2P5cfpHLOPVj1IjVwzv2dpMv85xqRFJX0vKTPSXqFvIpPU43/vH/e7ZL2++OT8p59t6QbK4x5QNJZ/rP8St57FJH0tKS7JF1ZZZ5vkNdWcEResOlfJf1759x7a3roys/xnKQLJH1T0jH/+oPyAm2vcs7dP834mp8pMDYr6R/8l//bOferuh4GAAAAAACsKOZc3f8gDgAAAAAAAABmxMyelrRF0p8457680PMBAAAAAABLB0EnAAAAAAAAAPPCzC6X9CNJY5LWOueGF3hKAAAAAABgCaF1HQAAAAAAAIA5Z2Z9kj7rv/w6IScAAAAAAFArKjoBAAAAAAAAmDNm9jlJfyBpjaSopBOSznXOHVvQiQEAAAAAgCWHik4AAAAAAAAA5lKfpPWSxiXdL+kyQk4AAAAAAGA2qOgEAAAAAAAAAAAAAAAAYNGjohMAAAAAAAAAAAAAAACARY+gEwAAAAAAAAAAAAAAAIBFj6ATAAAAAAAAAAAAAAAAgEWPoBMAAAAAAAAAAAAAAACARY+gEwAAAAAAAAAAAAAAAIBFL7LQE0B9zOy3kuKS9i/wVAAAAAAAAAAAAAAAAIBKNkoads5tquciBJ2WvnhLS0vP2Wef3bPQEwEAAAAAAAAAAAAAAADKPfnkkxofH6/7OgSdlr79Z599ds9jjz220PMAAAAAAAAAAAAAAAAAJjn//PP1+OOP76/3OqEGzAUAAAAAAAAAAAAAAAAA5hRBJwAAAAAAAAAAAAAAAACLHkEnAAAAAAAAAAAAAAAAAIseQScAAAAAAAAAAAAAAAAAix5BJwAAAAAAAAAAAAAAAACLHkEnAAAAAAAAAAAAAAAAAIseQScAAAAAAAAAAAAAAAAAi15koSeA+ZPP59Xf36+RkRGlUik55xZ6SpglM1MsFlNHR4d6enoUCpFZBAAAAAAAAAAAAAAAyxtBpxUin8/r4MGDSiQSCz0VNIBzTslkUslkUmNjY1q/fj1hJwAAAAAAAAAAAAAAsKwRdFoh+vv7lUgkFIlEtGbNGrW1tRGMWcLy+bzGxsZ05MgRJRIJ9ff3q6+vb6GnBQAAAAAAAAAAAAAAMGdIuqwQIyMjkqQ1a9aoo6ODkNMSFwqF1NHRoTVr1kgqfn0BAAAAAAAAAAAAAACWK9IuK0QqlZIktbW1LfBM0EiFr2fh6wsAAAAAAAAAAAAAALBcEXRaIZxzkkQlp2XGzCQVv74AAAAAAAAAAAAAAADLFakXYAkrBJ0AAAAAAAAAAAAAAACWO4JOAAAAAAAAAAAAAAAAABY9gk4AAAAAAAAAAAAAAAAAFj2CTsAc27lzp8xMe/fuXeipAAAAAAAAAAAAAAAALFkEnbDi7N+/X2amHTt2LPRUAAAAAAAAAAAAAAAAMENLMuhkZuvM7Otm9qKZpcxsv5n9VzPrruEae83MTbE0Vxl3jpn9jZkdM7OkmT1lZrvMrGWKe11sZj8ws34zS5jZL83sBjMLz+b5sbT82Z/9mZ588kldcMEFCz0VAAAAAAAAAAAAAACAJSuy0BOolZmdIelhSaslfU/SbyRdIOkvJL3JzC5xzp2s4ZK7quzPVrj3hZJ+LCkqaY+kg5Iuk/RfJF1uZpc751JlY94q6X9JSkr6jqR+SW+R9AVJl0h6Rw1zxRLU19envr6+hZ4GAAAAAAAAAAAAAADAkrYUKzp9SV7I6c+dc1c55z7inLtMXnDoLEmfrOVizrmdVZaSoJNffel2Sa2SrnbOXeuc+7CkC+UFmS6RdGPZmLikr0rKSdrunHuvc+4/Sdom6RFJV5vZv6v9LcBs7dy5U5s2bZIk3XnnnTKzieWOO+7Q3r17ZWbauXOnHn30UV1xxRXq6emRmWn//v2SpAceeEDXX3+9zjnnHMXjcbW0tOilL32pdu3apWQyWfGeZqa9e/eW7Dczbd++XSdOnND111+vU089VbFYTOeee65uv/32uX4rAAAAAAAAAAAAAAAAlpQlVdHJzDZLeoOk/ZL+v7LDN0u6XtIfmdmHnHNjDb796ySdLeknzrm/L+x0zuXN7D9Leruk/8fMbnXOOf/w1ZJWSfpr59zPA2OSZvZxSf8k6U8kfbvBc0UV27dv1+DgoHbv3q2Xv/zluuqqqyaObdu2TYODg5KkRx55RJ/+9Kf1mte8Ru95z3t04sQJNTU1SZJuvfVW/eY3v9HFF1+sK664QslkUj/72c+0c+dO7d27Vz/60Y8UDs+sK+Hg4KAuueQSNTU16eqrr1YymdSePXv0nve8R6FQSNddd13j3wQAAAAAAAAAAAAAAIAlaEkFneS1iZOk+51z+eAB59yImf1MXhDqInkhommZ2TWSNklKS3pS0o/L28+V3fve8gPOuefM7GlJWyVtlvTsdGMk/URSQtLFZharck802Pbt27Vx40bt3r1b27Zt086dO0uOF6ou3X///fryl7+s97///ZOu8aUvfUmbNm2SmZXsv+mmm3TLLbdoz549uuaaa2Y0n1/84hd673vfq6985SsT4agbb7xRL3vZy3TrrbcSdAIAAAAAAAAAAAAAAPAttaDTWf766SrHn5EXdNqqGQadNLma0jEz+1Pn3J5Z3HurvxSCTlXHOOeyZvZbSefKC0c9OdUkzeyxKodeMtW4mdr4kXsacZl5sf8zV8z5PbZt21Yx5CRJmzdvrrj/hhtu0C233KL77rtvxkGn1tZWff7zny+pAHXOOefokksu0U9+8hONjIyoo6Oj9gcAAAAAAAAAAAAAAABYZkILPYEadfrroSrHC/u7ZnCt70l6i6R1klrkBYY+7Y/9jpn9XgPu3cj5Yh5dcMEFVY+NjY3pU5/6lF71qleps7NToVBIZqa+vj5J0qFDh2Z8ny1btigej0/av379ekmaaKUHAAAAAAAAAAAAAACw0i21ik7TKfQSc9Od6Jz7QtmupyT9pZm9KOmLkj4l6Ydzce/ZjHHOnV/xAl6lp/NquCdmYM2aNRX3ZzIZXXbZZXr00Uf10pe+VNdcc41WrVqlaDQqSdq1a5dSqZl3Iezqqpxxi0S8P5q5XK7GmQMAAAAAAAAAAAAAACxPSy3oVKiA1FnleLzsvNn4mqQvSNpmZh3OuZE67j0f822I+WgHt5SYWcX93/ve9/Too4/quuuu0x133FFy7PDhw9q1a9c8zA4AAAAAAAAAAAAAAGDlWWqt657y11urHN/ir5+e7Q2cc0lJhXBTW533rjrGzCKSNknKSnpuVpPFrITDYUmzq5a0b98+SdLb3/72SccefPDB+iYGAAAAAAAAAAAAAACAqpZa0OkBf/0GMyuZu5l1SLpE0rikf57tDczsLEnd8sJOJwKHfuyv31RhzGZ5YabnVRpaqjpG0msltUp62Dk3815nqFt3d7fMTAcOHKh57MaNGyVJe/fuLdn/3HPP6cMf/nADZgcAAAAAAAAAAAAAAIBKllTrOufcs2Z2v6Q3SPpTSV8MHN4lrwLTV5xzY4WdZvYSf+xvAvs2S0o55w4Fr29mfZJu919+2zmXDRx+UNKTkl5rZlc65/7eHxOSdKt/zpedcy4wZo9/7N+Z2Redcz/3xzRLusU/57/V+DagTu3t7brwwgv10EMP6V3vepe2bt2qcDisK6+8ctqxb3nLW3TmmWfq85//vJ544gm94hWv0IEDB/T9739fV1xxxazCUwAAAAAAAAAAAAAAAJjekgo6+f6DpIcl3WZml8sLH10o6VJ5beM+Vnb+k/7aAvteK+lrZvagpGcl9Us6XdKbJXVK+rmk/xy8iHMuZ2bvllelaY+Z7ZF0QNLlkl4p6WeSvlA2ZtjM3icv8LTXzL7t3+tKSWf5+78zu7cB9fjGN76hG2+8Uffee6/uuusuOee0bt26iYpN1bS1tenHP/6xPvKRj2jv3r166KGHtHnzZt1000364Ac/qO98hy8nAAAAAAAAAAAAAAALIjUqnXxGOvGMlDgpXfQnCz0jNJiVFiBaGsxsvaRPyGsJ1yvpsKS7Je1yzvWXneskyTlngX2/I+lDks6XtFZSXF6rul9L+ht5VaHSVe59jrzqUZdK6pDXru4uSZ9xzo1XGXOJvADWqyU1S9on6euSbnPO5Wp/B0qu/dh555133mOPPTbleU8+6eW9zj777Hpuh0WIry0AAAAAAAAAAAAAYMVwTho9Kp14Wjr+lBdqOvG0twwHGnuFm6S/PCyFl2INoOXn/PPP1+OPP/64c+78eq6zJL+azrmDkt49w3Otwr4nJO2Y5b3/r6R31DjmZ/KqRQEAAAAAAAAAAAAAAGA6uYw0sL9CoOkZKTU0g/FpafB5qfeMOZ8q5s+SDDoBAAAAAAAAAAAAAABgGUgOF9vNHX+qGGbqf07KZ2q7Vigi9WyW+rZ6S6R5buaMBUPQCQAAAAAAAAAAAAAAAHPHOWnkcDHEFAw0jbxY+/VicalvSzHQVFh6NknhaOPnj0WDoBMAAAAAAAAAAAAAAADql8t4lZhOPO23nHu6GGhKj9R+vfhpfqDprNJgU8cayazx88eiR9AJAAAAAAAAAAAAAAAAM5cc8sJL5YGmgd9K+Wxt1wpFpd4zAoGmrf72FinWMTfzx5JF0AkAAAAAAAAAAAAAAAClnJOGX5ROPFUMNR33t0eP1H69WKe0auvkdnPdG6Uw8RXMDN8pAAAAAAAAAAAAAAAAK1U2LfU/W6zOdOIZL9B0cp+UHq39ep3rS9vMFZb21bSbQ90IOgEAAAAAAAAAAAAAACx344OBMFOw3dx+yeVqu1a4Seo9MxBoOsvb7j1TirXPyfQBiaATAAAAAAAAAAAAAADA8pDPS8OHytrN+YGmsWO1X6+5S1rlh5j6zvJDTVukrg20m8OC4LsOAAAAAAAAAAAAAABgKcmmpJPPlgWa/HZzmUTt1+s6fXKrub6tUlsf7eawqBB0AgAAAAAAAAAAAAAAWIwS/X6Q6Sm/5dwzXqBp8HnJ5Wu7VjjmV2baUhpm6j1Tamqdm/kDDUbQCQAAAAAAAAAAAAAAYKHk89LQwcqBpsSJ2q/X0hNoN7fVbzm3xavaFAo3fv7APCLoBAAAAAAAAAAAAAAAMNcy4367uafLln1SdrzGi5kXXFp1lh9m2uIHmrZKbb1zMn1gMSDoBAAAAAAAAAAAAAAA0ChjJyuEmZ6WBp6X5Gq7VqRF6jsz0GrODzT1niFFW+Zk+sBiRtAJK87+/fu1adMmXXfddbrjjjvm7b47d+7Url279MADD2j79u3zdl8AAAAAAAAAAAAAQIPlc9LgAb/d3NN+yzl/O3Gy9uu19lVuN9e5XgqFGj9/YIki6AQAAAAAAAAAAAAAAFBJOiGd3OeHmZ4pBppO7pOyydquZSGpa0PlQFNrz9zMH1hmCDoBAAAAAAAAAAAAAICVyzlp7ETldnODB1Vzu7loq9R7ph9oCrSb69ksRZvn5BGAlYKgE1aUQvs4Sbrzzjt15513Thy7/fbbtWPHDknSfffdp927d+vRRx/VyMiI1q1bp7e97W362Mc+pq6urpJr/vKXv9SnP/1pPfLIIzp8+LDi8bjWr1+v1772tfrsZz+raDSqjRs36vnnn5ckXXrppSXjnavxP4oAAAAAAAAAsFCyaWl8YIql31vnMlJzl9TSJbV0e0trT3G7sDS1S2YL/VQAAGClyOekgf2V282ND9R+vbbVXpBp1dbSQFP8NNrNNVgqm9NIMqvh8YxGkllvO5nRSDIzsX+4bP9oKqu//9PXKBTi583lhKATVpTt27drcHBQu3fv1stf/nJdddVVE8e2bdsmSfrEJz6hm2++WT09Pfr93/99rV69Wr/85S/1uc99Tj/4wQ/0yCOPKB6PS/JCThdeeKHMTFdeeaU2bdqk4eFh7du3T1/60pd0yy23KBqN6oYbbtDdd9+tBx98UNddd502bty4EI8PAAAAAAAAAJ5syvswL9E/dWBpYhn01unRxs4jFJ0cfgourVX2x+IEpAAAQHXpMT/AVBZoOrlPyqVru5aFpO5NZYEmP9TU0j03819mcnmnUT+ANBwIJnmBpUJAKaPh8axGUqXHh/1x6Wx+VvceS2fV0Rxt8BNhIRF0woqyfft2bdy4Ubt379a2bdu0c+fOkuMPPPCAbr75Zr361a/WD37wg5LqTXfccYfe/e536+abb9YXvvAFSV5VqGQyqbvvvltvfetbS641MDCg1tZWSdINN9ygwcFBPfjgg9qxY4e2b98+p88JAAAAAAAAYIXIjE8TWCqElgZL92USCz1zTz4jjR3zllpYeIpwVLByVFdZQKqT6goAACwXzkljx6XjT/lhpmeKgaahg7VfL9rmV2QqCzT1bJYiscbPf4lwzimRzk0ElAqBpOFAQKlacKmwfyydW7D5DycJOi03BJ3g2dm50DOYuZ1Dc3bp2267TZL01a9+dVKLuh07dmj37t361re+NRF0KmhpaZl0re5u0rsAAAAAAAAAZsA5L3hUCCFNGVoqW7LJ+Z2rhfzQUIU2dMGgUThaFq7qL60MVW/gyuWkxAlvqXX+zV0zCEiVLc2dUig8u7kCAID65LLS4PMVAk1PS8lZfHbcvsYLNK06q6zd3NplWTEylc15lZIKwaMqwaRJ+1NeoGk0lVUu7xb6MRQJmeItUXU0RxRv9tbFbX//xPHi/r72poWeOhqMoBMQ8Mgjjygajeq73/2uvvvd7046nk6ndfz4cZ08eVK9vb265pprtHv3bl111VW6+uqr9frXv16XXHKJzjjjjAWYPQAAAAAAAIAF5ZzX2m3KwNJgWfDHX2ptoVKvUGSawFJX5eBPU0djKyJlktOEuRrcQs/l/Wt8QCtsAAAgAElEQVT21zjQvLBTLeGolh5vTJiPYgAAmJHUqHTyGen4036gyV9OPutVgayFhb1KTJXazTUvnSIg2Vxeo6msRpJZDU1RMSkYTAoGmoaT2Vm3fGskM6k9VgwoxcuCSdUDS1HFmyPqaI6qORqSLcMgGmrHT9dAwMmTJ5XNZrVr164pzxsdHVVvb68uuOACPfTQQ/rkJz+pPXv26Bvf+IYk6ayzztLNN9+sd77znfMxbQAAAAAAAACN5JyUGq4SWBqcOoiTz87vXEPRQNimSsu2ioGl9sVRsSDaLEVPleKn1jYum6rwtagWjgp87VLDs5yok5KD3jLw29qGxjont9CrGpDqKX79wrRYAQAsQ85Jo0e9ANNxv81cIdA0fKj26zV1VG43171JiixsJR/nnMbSuYlWb+UBpEr7R8r2L2TLt6CWaLgsmFQMIMWrBJbiLZGJ4FJ7U0Sh0CL42RPLAkEneOawHdxS0tnZqXw+r/7+mf9rnle/+tX6/ve/r1Qqpccee0z33nuvvvjFL+raa6/VqlWr9PrXv34OZwwAAAAAAACgqnxeSg2Vhl0SM6kcNOi1RptP4VhZYGmG7dWirYsjsDTfIjGp4xRvqUUuUyUgNUW7wMSA9300W6khbxl8vrZxTR2lobXpqkcVzo3EZj9XAAAaJZeRBvZXCDQ9M7v/rnasDQSazipud5w6Zz8LJTO5qgGkkhZwUwSWFkHHN0XDVhJMKm39VhpIik8KLnnraLiBFT2BOhF0wooTDnt91HO5yb+ouOiii3TPPffo17/+tc4999yarhuLxXTxxRfr4osv1pYtW/THf/zH+t73vjcRdJrqvgAAAAAAAACmkM9JyfLA0hTBlInQyqCkef50Kdo6RRhlitBStGV+57lShaNS+ypvqUUuO/l7cKqAVOH7MzmkWX8Ppke8ZehAbeOibdO3IKzUtjDaPLt5AgBWtuRwhXZzz0j9z9Xebi4UkXrOmBxo6t0iNcdrulSh5dvweGkgabrAUvB4Orc4W76VB5PKA0vllZZiEVq+YXkh6IQVp7u7W2amAwcm/8/hjTfeqHvuuUfve9/7tGfPHq1du7bk+NjYmJ544glddNFFkqSHHnpIL3vZy9TZWdrH9ejRo5Kk1tbWiX29vb2SVPG+AAAAAAAAwIqQy3qtv6qFQqqFlpILUJG+qX1yC7jyYEh5YKm5i7DIchWOSG293lKLkpDe4NThqJKA1KDkZvnhambMW4ZfqG1cpKXse7pKVbFJAamWlVlVDABWEuekkcN+daZgoOlpb3+tYvFii7mSdnMbpXB0ouXb8LgfPDqS0XDyqB9Gyk7sLw0mFba9/YlF1PJtqmBSvGx/eWu4Nlq+AZMQdMKK097ergsvvFAPPfSQ3vWud2nr1q0Kh8O68sordfnll+szn/mMPvrRj2rLli1685vfrE2bNml0dFTPP/+8HnzwQb3mNa/RvffeK0n6q7/6K91///3avn27Nm/erPb2dv3617/WD3/4Q3V3d+v666+fuO+ll16qUCikj370o/rVr36l7u5uSdLHP/7xBXkfAAAAAAAAgFnLpmcRWBqQUsPzP9dYvEJgY4oWYK09XmAp0jT/c8XyEwp731OtPbWNy+e9Py8llaMqtdyr8Odutm0Xs+PSyLg08mJt48KxCq31ZvBnrqmNgBQALDbZtDTw28rt5tIjNV8u075W4/EzNNK+Sf2tm3Q8droORdbpWL5Lw4VQ0rNZDf86o5HkEY0kD2p4PKPRVHbRtHybVDEpVhpImiq41E7LN2BOmHOL4G8IzJqZPXbeeeed99hjj0153pNPPilJOvvss+djWovevn37dOONN+rhhx/WwMCAnHO6/fbbtWPHDknST3/6U91222366U9/qhMnTqizs1OnnXaaLrvsMl177bV65StfKUm6//77ddddd+lf/uVfdOjQIWWzWa1bt05vfOMb9aEPfUgbNmwoue83v/lNfe5zn9NTTz2lZDIpSar3zyBfWwAAAAAAAMxaNjWz4MTEMugFLtKj8z/X5s7pK8tMCix1eq3KgJXCOSk1MkVrvcHqf8ZrbS9Ur3DTFK0du6r/+Y51EJACgHolh4ohpmCgaeC3Uj5b06WyiuhQ+DTt11o9k1+rJzOn6qncGj3n1iqhhat0aSZ1xAKVlFpKW7p1TNMCLt5Myzeg0c4//3w9/vjjjzvnzq/nOgSdljiCTuBrCwAAAAAAAGXGp2+FVR50GO+XMol5nqjNvB1WeWApFJ7nuQIriHNSemzq1nqJKq0lc+n5nWsoMkVAaoqWe82dBKQALHv5vNNY2q+UNJ5Wqv+g8sefVvjkM4oN7lPbyHPqHNuvjsyJmq895Fq1z52mZ/Nr9axbq33OWx90q5VT439Oa20Kl7Rw8wJIhTCSF0SaKrBEyzdg8WlU0InWdQAAAAAAAACwGDjnB5aqhQz6K4SV/CU7Pr9ztdA0QYNK1Vm6vZZwIdp3AIuOmRRr95au9TMfN93fWyV/dzXo7618Vho77i21sHCNQUv+3gIwv5xzSmXzGk5mNDye1Ugy4wWW/PVIhf3DyazGx8fVmTig1enntTb7gs6wQzrDXtQZ9qLaLFXzPF5wfRNhpmfdWu3Ln6Zn3VqdUFzSzIJDwZZvFVu8NQcCS4Hjhf20fAMwFYJOAAAAAAAAANBIE5VRpvrgv8r+XO0fRtVlysooUyyxOB/8A/ACUk2t3tK5rraxs6pENyBlxmY3V5eTEie9pSbVKtHNoHUmleiAFcU5p/FMTgOJjAYTaQ2NTxNYSvn7x4vHM7nq3ZjiGtWZ9qLOCL2orXZ4ItB0uh1TxPLeSTP89D/lonrOrZkIMz3rh5mec2uUsma1x0orKG2bqJ4UCVRVqh5YouUbgLlE0AkAAAAAAAAAKnFOSo1MEVgarPBhvL/kM/M711C0+OF6xaokVT6Qj3XQygnAwoi2eEt8bW3jMkkpOTjDgFQhJNUvpUdnOVFXvFatmjtra9NZqCAV5uM7YKElMzkNJjIaHE9rYCyjofG0H2DyQkyDiYwGEmkNjhdfDyYySufydd3XlNep6teZoWJVpjND3nqVDdV8vRHr0OGm03WieYMGWzdqrOMMJbvOkHWdro7WZnU0R3Rxc1RvCgSWaPkGYLHjJyUAAAAAAAAAy1s+L6WGywJLgzP4cHzAqwAyn8JN3ofgJYGl8koiFT4kb2ojsARgZYg2S9E1Usea2sZl05UDUtX+/i8sqeHZzzU55C0D+2sbF4uX/d0/TfWoQkAq0jT7uQLLVDqb1+B4MYg0kEhrqEJIaSAQVhocTyuZqS+wNJ2Y0tpgR3Vmoc1c6EWdaS9qsx1Wa43t5pxMufg6ud6tCq8+S6FVW6VVZ0l9W9XR2qsOM22do+cAgIVA0AkAAAAAAADA0uScNHpMGjooDT4vDR70todekMZOFD+kTg5Kbm4/rJok0lJWYWmGbY+iLQSWAGAuRJqk9tXeUotcxgsrzTgc5R9L1l55ZUJq2FsGD9Q2rqm9+N+c5i4vPGsmyaZZa4bnTbVWneOXyzxCi+xZFsN70pjrZPNOQ8msBhJZDSUzGhjLTA4qlVVXGkykNZae39B6LBJSd2uTulq9tm+nRsd1hh3SBndIa7MHtTq1Xz3jz6s9cUimGn8+jTRLvWdKfVv9ZYu06ixZzxmKNLXOzQMBwCJE0AkAAAAAAADA4pTLSiOH/SDTAT/I5K8HD3iBplxt/+K9ZtG2QNWMKtU0JoWWurzAEgBg6QtHpbY+b6lFPlcakJpJOKpQcVBudnNNj3rL0MHZjQcWsYikXn8pyDuTk1fRyFu8bRW2zeRik8+RKo8rPW4yKywhbx0yhfztUCikUGEdKqy9fROht6GTUuJE7Q/b2hsIM/nLqq1S53opFJ71ewgAywVBJ2AJc26W/7MDAAAAAACwGGRTXlipJMh0sBhkGj7UuNZxTR2llZVaq7UBKgssRWKNuT8AYGUJhb3/1rT21DYun5dSwYDUFG31xsvCU/NdvRBYYCErfE42h5+XucDlG1ocyqTuDZMDTX1bpbbe6YcDwApG0GmFMDM555TP5xUKhRZ6OmiQQtDJKGcOAAAAAAAWo/RYILz0/OQg0+hR1f3BVHOX1LVe6jzdW3ed7v1r9/bVgdBSl1eRAwCAxS4UKgZua5HPS+mRYvWo5KBXVcr5KY1ZrVXn+EZeR4t8Hvl5mkPjn8E5p7zLK5/LK+d/lugt/rbztt3E2tt2/vhC/SQrraVUcX9he2bHJ+8vBpuWkEiL1Hem1HdWsd1c31avBV20eaFnBwBLEkGnFSIWiymZTGpsbEwdHR0LPR00yNjYmCTv6wsAAAAAADCvnPM+RJ0IL1VoLTfeX/992lb7Qab1fpBpQ3G7c73UHK//HgAALHWhkNTc6S3dGxd6NlgAzjklM3kNJNIaTGQ0mEhrcDxT+jqR0UAio6HxtAbGMxr0tzO5+Q0QdTRH1N3apK7WqLpam9TVElV3a1SdrU3qbo2W7ffOizdHpZAVHnYRhfCmuE5TqxRf5/35BAA0DEGnFaKjo0PJZFJHjhyRJLW1tU30lcXS4iXlncbGxia+noTXAAAAAABAwzknjR2fHF4qVGQaOiilhuu7h4WkjrVlQSa/IlPX6VLnOina0pjnAQAAWCKSmZyG/CBSpeDSUMn+jAbH0xpIZJTOzm/7wvZYRJ0tUXW3RdXVUggueeGkzkBIKRhc6myJKhKuM/hj5i0AgBWJoNMK0dPTo7GxMSUSCb3wwgsLPR00UGtrq3p6auzxDQAAAAAAkM9JI4fLKjIFg0wvSNnx+u4RinphpUqt5brWS/HTaCkHAACWrXQ27weW/JDSmLeeVF1pLFOyfzyTm9d5tkTDpVWU2qLqbKlQXanNW3f5QaamCJWKAADzj6DTChEKhbR+/Xr19/drZGREqVTK752LpcjMFIvF1NHRoZ6eHoUoeQkAAAAAAMpl09LwC4EgU7Ai0wFp+JCUz9Z3j0iLF1yq1lqu/RQpFG7M8wAAACyQbC6v4WS2Qhu4tIbGK1RXGstoaDyj0VSdP2vVqCkS8sJJgepKXS1N6vIrLpUElwKVl5qj/LwGAFg6CDqtIKFQSH19ferr61voqQAAAAAAAKBe6USg+lKF1nIjhyXV+Q/dYp2TqzAFW8u19tI2BAAALBn5vNNwMtASrry6UsJrARfcP5hIazg5v4GlSMgCYaQK1ZX8kFKhulLhdXM0JONnMwDAMkfQCQAAAAAAAFiMkkN+O7kqreUSJ+q/R2vf5PBSYd21XmrurP8eAAAADeac00gqq6FEsZrSRHWlMa+qUiGk5LWHKx6fz4YnIdNE27dgSGmq6krdbU1qawoTWAIAoAqCTgAAAAAAAMB8c05KnJwcXpoINB2UUkN13sSkjlMnV2TqPN1/vU5qam3I4wAAAMyGc06JdG5y6ze/utJEpSV/30SVpfGMcvn5SyyZSfFmv7pSqx9SKqum1NUa9YJKhTBTa5M6YhGFQgSWAABoJIJOAAAAAAAAQKPl89LokUA7ubLKTEMvSJlEffcIRaT4aX71pWCQyQ82xU+TIk2NeR4AAIBpJDO50upKfkhpUnWlQNu4oURG6Vx+XufZEYuoq82rqlQIJFUKLnUG2sPFW6IKE1gCAGBRIOgEAAAAAAAA1CqXkYYPBYJMhYpMhQpNh6R8pr57RJqL4aVKreU61kihcGOeBwAAwJfK5jTkV00aGPMCSYPl1ZVK2sN5waVUdn4DS21N4WIruNZgcCnQBi5QXalQcSkaDs3rPAEAQGMRdAIAAAAAAADKZca9qkvVWsuNHJZcnR/mxeIVgkyB1nJtfV6fFAAAgFnI5PIaKgspFVu/lVVXKhwbzyiRzs3rPJujoUkhpYlwUktZdSX/nM6WqGIRAt8AAKxEBJ0AAAAAAACw8iSHy8JLz5cGmcaO13+P1t5AkGnD5NZyLV313wMAAKw4ubzTydGUjgwndWQoqaPDSX87NbF9dDipkWR2XucVDVugDVxZdaVAG7hge7iu1qiaowSWAADAzBF0AgAAAAAAwPLinJTo99vIBcJLE63lDkrJwfrv03Hq5PBSsDJTU1v99wAAACtKIp3VkaFiWGkivBTYd2wkpVzezdkcwiFTt181aVJ1pbbylnDFY61NYRnVKAEAwBwj6AQAAAAAAIClJZ+XRo8WA0wlQSa/SlNmrL57WFjqPM1vIxcMMvnbneukSKwxzwMAAJa9fN7pxFhKR4f8SkzDSR0tCTR5242swhQyqTNQQalyG7hie7hCcKk9FiGwBAAAFi2CTgAAAAAAAFhccllp+FD11nJDL0i5dH33CMcCAab1fqApEGTqOFUK86szAAAwvfF0rkIbueL20SGvClO2gVWYulujOiXerDWdzVoTb6643dUSVShEYAkAACwv/LYGAAAAAAAA8yuT9IJMwfBSsCLT8IuSy9V3j6b20jZy5a3l2lZJoVBjngcAACxL+bzTybH0pNZx5dvDDazC1BQOaXU85gWW/OBS+fbqeEzN0XDD7gkAALCUEHQCAAAAAABAY6VGA+GlA8XKTIXt0aP136OlOxBkOn1ya7mWbomWKwAAoIpkJjdleOnocErHRpLK5BpXhamrNVqsuBQML3XGJvb1tDXRNg4AAGAKBJ0AAAAAAAAwc85J4wOTqzAFQ03jA/Xfp/2UChWZNhS3Y+313wMAACw7+bxTfyI9qXXckeGkjgynJraHxjMNu2c0bFrdUd46rhheWtPp7aMKEwAAQP0IOgEAAAAAAKDIOWn0mB9eCraWC1RkSo/Wdw8LS/G11VvLxU+Tos2NeR4AALBsJDO5CtWXUhOBpiNDyYZXYepsiQaqL5W2lDvFDzH1tDYpFKIKEwAAwHwg6AQAAAAAALCS5LLSyOGyikwHAkGmF6Rcqr57hJukznXVW8t1rJXC/FoKAAB4nHPqH0uXhJeClZgKQabBROOqMEVCplPizTolHpuouBSsvlQIMrU0UYUJAABgMeE3SgAAAAAAAMtJNuWFlSa1lvNfDx+SXK6+e0TbvMBStdZybaulUKgxzwMAAJa0ZCanY8Mpv3VcsI1ccfvYcErpXL5h94w3R6qGlwrbvW1UYQIAAFiKCDoBAAAAAAAsJemxQHjpQLGdXCHINHpUUp3tWpq7/CDThsqt5Vq6JeODQQAAVjLnnAYSGR0ZSpa0jivfHmhwFabVHbFJreOC26fEY2pt4uMvAACA5Yqf9AAAAAAAABYL56Tk4OQqTEOBykyJk/Xfp211WUWmstZysY767wEAAJasVDZQhakQXiprI3d0OKV0tnFVmDqaI5Nbx/khJm87pr62GFWYAAAAVjiCTgAAAAAAAPPFOWnsuB9aOhAIMh0sBplSw/Xdw0JSx9rqreU6T5OiLY15HgAAsKQ45zSYyExqHVcMMqV0dDip/rF0w+4ZLlRhKm8j11m6jypMAAAAmAl+agQAAAAAAGiEfF5KDUmJfmn0WOXWckMvSNnx+u4Tikqd6wJBptNLW8vF10rhaGOeCQAALBnpbF5Hhyu1kUuVBJpSjazCFIuUtZGLTWop19seU5gqTAAAAGgQgk4AAAAAAADlsmlpfEAa7/daxSX6/e3CesDbH9w3PiC5BnxwGG0tVmGq1Fqu/RQpFKr/PgAAYElwzmloPFPWRi5VUonp6HBSJxtYhSlk0uqOQuu4WIU2ct66LcbHTAAAAJhf/AQKAAAAAACWL+ek9FgxkJQ46QWSJgWX+gPBpQEpPTJ3c2runFyFKbjd2isZVQ8AAFgJ0tm8jo1UDi8Vto8OJ5XMNK4KU3ssolPisWILuWA7OX+7jypMAAAAWKQIOgEAAAAAgKUhn5OSQ4FwUqVKS35lpeCxXOOqG0wrFpdauqW2Pq+9XOd6qWtDIMi03gs6AQCAZc05p+HxrFeFaTg50TouuH10OKkTo42twrSqo7R13KQgU2ez2qnCBAAAgCWMn2YBAAAAAMD8y6aqVFPqn1xxqXBsfFCSm5/5WdgLLLX2SC09peuK+3q988PR+ZkfAABYMJlcXsdGUoE2cv66bLuRVZjamsIVW8cVwktr4s3qa29SJEx7WwAAACxvBJ0AAAAAAMDsOSelRsqqKg2UBZfKW8T1S5mx+ZtjpNkPIvVIrd1l4aTy4JIfbop1SiE+KAQAYCVxzmk4mS1tHReovnTEby93ciwl16DstZm0qn2qNnIxnRJvVkczYWoAAABAIugEAAAAAAAK8rnJ1ZQqtogbCFRaGpDymfmbY3NnhXDSFJWWWnqkptb5mx8AAFiUsoUqTFXbyHkVmsYzuYbdsyUa1qmd5W3kYiVt5Fa1x6jCBAAAANSAoBMAAAAAAMtRZrx6NaWKlZZOSsmh+ZtfKFK9mlKLX22pPLDU0i2F+VUGAAAocs5pJJUthpeGSqsvFbZPjDa2ClNvW0xrOmPF9nGBdnKFIFO8OSIza8xNAQAAAEgi6AQAAAAAwOLmnBdAKrSEm7LSUqBtXHZ8/uYYbZ26qlKltnGxuPcpIQAAQBXZXF7HR1PF8NJQUkeGUxPbhRBTIt24KkzN0VAxvOQHl0oqMnU2a3VHTFGqMAEAAAALgqATAAAAAADzJZep0hqu0AauUpipX3KN+/Buaua1hisJJ/VMDimVH4s2z9P8AADAcjGSzPiBJb+d3ESQqbh9YjSlfIOqMElSX3vT5OpLZdvxFqowAQAAAIsZQScAAAAAAGYjnQiEk4LBpYHqlZZS89kaLlpWValCpaXyFnEtXVIoPH9zBAAAy854Oqf+RFrHR1IlVZcmWsv522MNrMIUi4SKFZeC1ZfizVrTGdMp8Wat7mhWU4QqTAAAAMBSR9AJAAAAALCy5fNeAClRHlgqr7TUX1qNKZucvzk2tZdVVeqtEFjqLq201NROazgAAFCXdDavwURa/Ym0+se8ZWAsrf6xjAb8fRPrMe+8ZCbf0Dn0tjWVto4LhJcKreU6W6JUYQIAAABWCIJOAAAAAIDlI5suVlSaVGmpv3KYaXxAco39QK46k1q6yyot9U7eV34sEpun+QEAgOUql3caTBSCSZmKISVvndGAv28klZ2z+TRFQmWt40rDS6fEm7U6HlMsQrVJAAAAAEUEnQAAAAAAi49zUnrMDySdnFxNaWJ9srQ1XHpk/uYYjpWFk7oD4aQqreKau6QQLVMAAEB9nHMaTmZLA0pjxRBTaXDJOzY0npFzcz+3pnBIPW1N6m5r0pp4rKQS0yl+iGlNvFldrVRhAgAAAFA7gk4AAAAAgLmVz0nJoSrhpCkqLeXS8zfHWLxKVaXe0hBTsNJStJXWcAAAoG7OOY1ncn5lpUxJcKm/SpBpMJFWNj/3qaVwyNTdGlV3qxdc6ims27x9hUBTT2C7rSlMgAkAAADAnFmSQSczWyfpE5LeJKlX0mFJd0va5ZwbmOU1XyvpAUkhSZ90zn287Pgdkq6b5jI/ds5dHhizQ9LtU5z/J865L89mvgAAAMD/z96dB8d1nne+/53esXSfBhcABBoSRUoUJRKARFK2E9tJLDsab+MtkjdZpmTPeKaSSTKZ1L11q5y5GedWKjU1qdQkmcxMFiei95iyLcVLHI8d2/G1rycWKKLBRaS4SWgQ4AKwu7H1/t4/TgNsoBtEE2o00MD3U4U6YL/nfftp/kEC6B+eB1gTufQtAks3Kq/NxiXVoX2AJFmuJboptS0KLpWsNbVJHl996gMAABteOpdXfCar8amS0XALRsRlFwWXMkrn6jNG127yOoGk5rlrSVhpQZDJ+TwY8MjlIrQEAAAAYP1ouKCTZVm7Jf1EUruk5yS9KOk1kn5T0lsty3q9MWb8Ns8MSjoiaUZS6xK3PSvp0hJrT0jaJenvl1h/TtLxCo8/X32VAAAAAFBDxkjpyeW7Ks0UQ0tzIabsdP1q9ARKxsC1lQeYFoyIK3Zc8tuMhgMAADWTyxcUn10cTMouCi6VXrOaSufqUluLz30zlLQopOQEmBZ2XQo3eeVx83USAAAAgMbWcEEnSf9dTsjpN4wxfzr3oGVZfyTptyT9vqR/e5tn/rEkW9IfFPeXMcY8KyfstIBlWWFJ/6ekjKSnlzj/WWPMUmsAAAAAUBu5jBR/WZq4KE1dKQ8szd4o6bR0Qypk61eb33bCSgvCSaUhpQqdlnzN9asPAABseIWC0WQqp4klQ0pOiGliOq0bM1lNTGeUmK3P10s+j0tbF3dYavYuCi7dvIabvQp43XWpDQAAAADWk4YKOlmWtUvSI3I6K/3ZouXflfQJSU9YlvXbxpiqfs3Ysqx3S3pKTlemlfx9PCGpSdKXjDHXV7AfAAAAAKqXS0s3LkkTF5yP8fM3P08MS2aVx55Y7pJA0taF3ZQqdloqrrsb6ttPAACwzhljNJPJLxwLN5MpGReXXRBgujGT0Y2ZrPKF1R+n63ZZFTsq3ey45F0QXNra6lOT1y3LYkQcAAAAACyn0X7S/HDx+h1jFv703hgzaVnWj+UEoV4n6XvLHWZZVrukv5TTcelzlmU9uYKa/nXx+he3uOcBy7L+vaSApBFJ3zfGxFbwXAAAAAA2g2xKunHxZoBpPtB00QkzqUZv0HmbF42Fq9BVqXnR5/6QxJtwAACgxlLZfMk4uGxJh6WFQabSAFMmt8oB76Jws3c+pDQfYGrxVejA5FxDAQ+hJQAAAABYJY0WdLq3eD27xPpLcoJOe1RF0ElOOMml2x91J0myLOvnJPVKOmuM+f4tbv3NRX/OW5b1V5L+vTEmVeVzDSyxtLea/QAAAADWmczM0mGm5IhWFmayJDsibblLCnYVg0tti4JLJWEmb6DWrwoAAEC5fEE3ZrIlwaWFo+EWBpecx6cz+brU1ur3qK3Fqy0t/puj4eY7Ld0MLs11XbKbvPK4XXWpDQAAAACwvEYLOtnFa2KJ9bnHw8sdZFnWxyS9W9IHjDFXVljPJ7Up+RkAACAASURBVIrXv1xi/aKkX5f0HUkxOfW/QdIfSPo3kkKSPrzC5wYAAACw3mWmneDSRMl4ufHidfLyCg+1pHCPtGVX8WP3zc/bdhJeAgAANVUoGCVT2YodlW5MZzReFmTKKJnK1aU2v8fldFVaFFIq7bpUGmIKN3vl97jrUhsAAAAAYHU0WtBpOXP9gG/5q8+WZe2U9F8lHTXGfHlFT2RZtqT3S8pIerrSPcaYH0r6YclDM5KOWpb1U0mDkj5kWdZ/NsYMLvd8xpiDS9QxIOnA7VUPAAAAoGbSk0uHmabGVnam5ZLCdywRZrpT8vhr+xoAAMCmYIzRdCY/H0iaqDgabuHouBszGRVqNDX3VjwuqySY5K0QXLo5Hm5Lq3Nt8hFaAgAAAIDNptGCTnMdm+wl1kOL7lvKX0ualfSrr6KWj0hqlvQlY8z129lojBm2LOtbkh6X9AtyQk8AAAAA1qtUshhimgszXSyOmbsgTV9d2ZmW2wktVQozhe+QPL7avgYAALDhpLL58rDSdEYTM9myLks3ZpwAUyZfWPW6LEsKNy3qqFQSUGorGQ03F2AK+j2yLGv5wwEAAAAAm1qjBZ3OFK97lli/p3g9u8w5B+SEpa4t8c3zJy3L+qSk54wx71nijH9dvP75Ms+1lGvFa8sK9wMAAACopdn4zY5MpR/j56WZ2/rdhptcHmec3HyYaS7QdJcTZnJ7a/oSAABA48rmC/NhpPLg0qIAU/FjNpuvS23BgKeq0XBz63aTV24XoSUAAAAAQO01WtDp+8XrI5ZluYwx879+ZFlWUNLr5XRq+uky53xGTjemxe6R02HpuKQBSS9U2mxZ1msl9Us6a4z5we28gBKvLV4vrHA/AAAAgNs1M1EcM3dh0ai589LsxMrOdPsqhJmKH3aP5G60b7sAAMCrVSgYJWazZR2VJqazmphOa2I6WzYubjKVq0ttTV53sYuSd8nRcHOj47Y0+xRu9snncdWlNgAAAAAAltNQP3E3xpy3LOs7kh6R9GuS/rRk+VNyuiP9uTFmeu5By7L2Fve+WHLOb1Q637KsJ+UEnb5pjPmdW5TyieL1L25Vr2VZbzTG/GjRY5ak/0vSz0m6LunbtzoDAAAAwG0wphhmulA5zJSKr+xct9/pwjQfYrrr5qg5OyK53LV9HQAAYN0wxmgqnVs0Iq7yaDjnmlV8JqOCWf3avG6rQljJu6DL0uJOTE0+vm4BAAAAADSuhgo6Ff2qpJ9I+hPLst4s6bSc7khvkjOy7pOL7j9dvNakV7JlWSFJH5CUkXRkmdv/ybKss5J+JmlEzri810vaL2lG0uPGmGQt6gIAAAA2DWOk6euVw0wTF6RUYmXnegKLgky7boaZQl2EmQAA2CBmM/nKAaX54NKi0XEzGWXzq59asiyprdmntmbvfChpa+vicXELuy61+j1yfq8SAAAAAIDNoeGCTsWuTock/Z6kt0p6u6RRSX8i6VPGmBXOm6ja43I6R33JGHN9mXv/UNJrJD0saYukgqRXJP2ZpD8yxjC2DgAAAKjEGGnq6hJhpotSeoW/L+BpcoJLW0tHzBXDTMEdkouxLAAANBpjjCbTOY0lUs5HMqUriZTGS8JKpUGmVLZQl7pCAc+iYFJpYMlbFlwKNXnldhFaAgAAAADgVhou6CRJxphhSU9VeW/VPx0wxjwt6ell7vkfkv5Hlef9H9U+NwAAALDpGCNNji3sxjQfaLooZaZWdq635RZhpk6nXQIAAGgI+YLR+FRao3MBpmRKowknyDSWvBlsmsnkV7WOZp97UTDJu0SAyem01Nbsk9dNgBoAAAAAgFpryKATAAAAgAZRKEhTY9L4+UVhpovO59mZlZ3rCzrj5bbuLg8ztbYTZgIAoAGksvmbwaViaGn+8+Kfr06mlS/Udmycz+2aDyOVdVxq9mpLq39+NNxcgCngZYQtAAAAAADrAUEnAAAAAK9OoSBNXl4UZioZM5ebXdm5/lCxM1OFMFPLNsJMAACsU8YYJWazGlui+9LcNT6TrdlzBrwu7bCb1BHyqzMUUIcd0PZW/4Ig09znLT63LL6OAAAAAACgIRF0AgAAALC8Ql5KjiwdZsqnV3ZuwHbCSwvCTMVAU/MWwkwAAKwzuXxB16bSTlhpLrg0F14q6caUyhZq9pxtzV51hALaYQfUaQfUGWpSp+0vPtakzlBAoSYP4SUAAAAAADYBgk4AAAAAHIW8lBguCTNdvDlq7sYlKZ9Z2blNW24GmBYHmpq31PQlAACAlZvJ5Mq6Ll1ZNE7u2mRatZok53FZag/61WE7IaaOUECdobkwk3PtCAUYGwcAAAAAAOYRdAIAAAA2k3xOSrwijS/uynReuvGyVFjhCJnmbRXCTHc516a22r4GAABwW4wxujGT1WhiVlcWjZObDzElUkqmcjV7zmafe0FgqdJ1a6tfbhddmAAAAAAAQPUIOgEAAAAbTT4rxV9xAkwLRs2ddx4vrPBNzJb2kjDTroWdmQJ2bV8DAACoSjZf0JXkXFgpPR9mGkumnW5MyVldSaaVydVulNy2Vl9Z96W5rkxznwf9jJIDAAAAAAC1R9AJAAAAaES5jBR/uUKY6YITZjL5lZ3b2rl0mMkfrO1rAAAAtzSVLo6Smxsjl0xpNDGrsUR6vjPT+HRapkaj5Lxuaz7A1GEHtKNkfNzcaLn2kF9+D6PkAAAAAADA2iDoBAAAAKxX2dTSYabEsGRW2Jkh2HVztNz8mLldUttdkr+1tq8BAACUKRSMxqcz82GlsaQzSm5+jFxxlNxUunaj5IJ+j9N9aVFwab4rkx3QlmafXIySAwAAAAAA6xhBJwAAAGAtZWelG5duBpjmA00XnTCTVtiiIRRxgkxbdi0KM+2UfC01fAEAAKBUOpfX1WRaY8UQ05XEzeDS3PXqZErZfG3aMFmWtK3VXx5cKgkwdYYCavHzY0AAAAAAAND4+AkHAAAAsNoyM9KNi5XDTMkRrSzMZEl2z9JhJm9TjV8EAACbmzFGyVTO6bhUMk5urOTPV5IpjU9navacPo/LCSyVhJYWdGOyA2oP+uV1u2r2nAAAAAAAAOsZQScAAACgFjLTC0fLjZ93gkwTF6TJyys703IVw0y7ysNM4Tslb6C2rwEAgE0qXzC6PpVe0HWp0ji5mUy+Zs9pN3nVGQqoww5oR/HauSjE1NbslWUxSg4AAAAAAGAOQScAAACgWunJRWGmks+nxlZ2puWWwnfcDDCVBprCd0gef21fAwAAm0wqm78ZXCqOk5vrvjQXYro6mVa+UJtRci5Lag/OBZf82mE3FYNLfnWGmuZHyTX53DV5PgAAAAAAgM2EoBMAAABQKpWUJs5XDjNNX13ZmZZbartT2rK7PMxk90geX21fAwAAm4AxRonZrBNcqtB9aS7cFJ/J1uw5A15XMbjkL46Ta1JnyF8cK9ekzlBA21p98jBKDgAAAAAAYFUQdAIAAMDmMxsvhpkuLho1d0Gaub6yM13eCmGmXTfDTG5vbV8DAAAbWC5f0LWptBNcWjROrvSazhVq9pxtzd6FwaVQkzptvzpCAe0ohphCTR5GyQEAAAAAAKwhgk4AAADYmGYmikGm8+VhptmJlZ3p9kltO0vCTHfd7MwUikhuvrwGAGA5M5lcWWhpcTema5Np1WiSnDwuS+3Bua5LgWJwybl2FkNM7SG/Al5GyQEAAAAAAKx3vBMDAACAxmRMMcx0oXKYKRVf2bluvxNg2rK7eC0ZMxfqlly8CQoAQCXGGE1MZ5zgUjK1oBvTfIgpkVIylavZc7b43OqwFweXip8Xg01bW/xyu+jCBAAAAAAAsBEQdAIAAMD6ZYw0fb08zDRxQRq/IKUTKzvXE7g5Xm5+zFwxzBTsklyu2r4OAAAaXCZX0NXJubBSWqOJ2WL3pbTGErPFcFNamRqOktvW6psPLzmj5G6Gl+Y+DwYYDQsAAAAAALCZEHQCAADA2jJGmrpaEmIqDTRdlNLJlZ3rbS4PM80Fmlo7CTMBAFA0lc45YaVEujhObrZ4TWss6Tw+Pp2WqdEoOa/bmg8wddgB7QiVj5RrD/nl99BFEQAAAAAAAAsRdAIAAMDqM0aaHFs6zJSZWtm5vtaSMXOLw0wdksWYGgDA5lUoGI1PZzRWHB83H2JKpIuj5WZ1JZnWVLp2o+SCfs9816XS4FJpN6YtzT65GCUHAAAAAACAFSDoBAAAgNq6cUm68MOSMNNF55qdWdl5vqC0dVflMFPLdsJMAIBNKZ3L62oyrdFiiOlKIqXRRHG0XDKlsURKVydTyuZr04bJsqRtrf7y4NKicXItfn7UBAAAAAAAgNXDT58AAADw6uUy0plvSQNPSxe+f/v7/XYxzLQo0LR1t9S8lTATAGDTMMYomcoVOy45AaaxZEmIqfjnielMzZ7T53GVB5cWjZPbHvTL62bsKwAAAAAAANYWQScAAACs3Ph56dhnpOOfl6av3frepraSjkyLwkxNbYSZAAAbXr5gdH0qrbEK3ZfGin8eTaQ0m83X7DntJu+C8FKH7QSXOkM3Q0zhZq8s/h8GAAAAAABAAyDoBAAAgNuTS0svfsPp3nTxnyrcYEl3v1mKvKYk2HSX1Lyl3pUCAFB31ybTisbieunq1ILw0pVkSlcn08oXajNKzmVJ7cFicGlR96WOkmBTk89dk+cDAAAAAAAA1gOCTgAAAKjO9XPSsael41+QZsbL14Nd0oGPSg9+RAr31L08AADqLTGb1VAsocFYXEOxhKKxuC4nUq/63IDXpR12kzpC/mI3piZ1hvxOeMluUmcooG2tPnkYJQcAAAAAAIBNhqATAAAAlpZNSae/Lh07Il36Ufm65ZLu+RfSwSelu98iufnyEgCwMc1kcjp5OanB4biisYSGRhK6eH36ts9pa/YuDC6FmtRp+4vdmJwQU6jJwyg5AAAAAAAAoALeiQIAAEC5a2ekgSPS4Bek2Rvl63aP073pgcclu7v+9QEAsIoyuYJeHEtqMJZQtBhseunqpKqZOhfwurSvy9a+rpAibU3OGLliiKk95FfAyyg5AAAAAAAAYKUIOgEAAMCRnZVOPecEnF75Sfm65ZbufZvTvWn3w5KLN2oBAI0vXzA6d3VKg7G4ojEn1PTi6KQy+cKyez0uS3t3BNUXCas/Yqu3O6w9Ha2MlAMAAAAAAABWCUEnAACAze7KKWc03eAXpVSifD18R7F700ek0I761wcAQI0YY3RpfGY+0BSNxXViJKnZbH7ZvZYl3b291Qk19djq7bZ1344QHZoAAAAAAACAOiLoBAAAsBllZqRTz0oDT0vD/7t83eWR7n27071p15skF50pAACNxRij0URqPtA0d02mclXtv2NLs/oitvojYfVGbO3vttXq58coAAAAAAAAwFriJ3QAAACbydgJJ9wU/bKUrtC9qW2ndOCw9MDjUrCj3tUBALBi41PpYpjJCTQNxhK6PpWuam9HyD8/fq4vElZvt622Ft8qVwwAAAAAAADgdhF0AgAA2Ogy09KJrzoBp5Hny9ddXum+dzrdm3b+At2bAADrXjKV1YlYQoOxhIZG4hocTmgkPlvV3nCzd0GoqS9iqyMUWOWKAQAAAAAAANQCQScAAICNanSw2L3pqJSZLF/fsssJN/V/WGrdXu/qAACoymwmr1OjCQ0OF0fQjSR04dp0VXtbfG7t77bV3xOeH0MXaWuSZVmrXDUAAAAAAACA1UDQCQAAYCNJT0onvuIEnC6/UL7u9kn3vavYvekNEm/0AgDWkWy+oDNjkxqMxRUdTig6ktDZK5PKF8yye30el/Z1hdTX7XRq6u+xtWtbq1wu/q8DAAAAAAAANgqCTgAAABvB5ReccNPQM1Jmqnx96z3F7k0fklq21rs6AADK5AtGF65NaTDmdGoajCV0ejSpTK6w7F63y9K9HUH199jq7Xa6Nd3bGZTXzfhVAAAAAAAAYCMj6AQAANCoUklp6KgTcBqLlq+7/dK+90gHDkt3/jzdmwAAa8YYo+GJWadTUzHUdHIkoelMftm9liXt2tai/khYvRGnW9O+rpACXncdKgcAAAAAAACwnhB0AgAAaCTGSCMDTrjpxFek7Ez5Pdv3Ot2b+j4gNW+pd4UAAGgskVI0Flc0ltBgLK6hkYTiM9mq9kbamtQfcbo09UZs9XbbCga8q1wxAAAAAAAAgEZA0AkAAKARzMZvdm+6cqJ83ROQ9r3XCTj1vJbuTQCAurkxnXHCTLHE/Bi6q5PpqvZuD/rVX+zS1Bux1ddta2urf5UrBgAAAAAAANCoCDoBAACsV8ZIsZ8Vuzd9VcrNlt/Tfr908Cmp7zGpqa3uJQIANpepdE5DxTBTdMS5Dk9U+P+pArvJq76IXfxwOjZ1hgKyCOcCAAAAAAAAqBJBJwAAgPVm9oY0+LdOwOna6fJ1T5O0/1ec7k2RQ3RvAgCsilQ2r1OjSUWH50JNCZ2/NiVjlt/b7HNrf1cx1NQTVl+3rTu3NhNqAgAAAAAAAPCqEHQCAABYD4yRXvmpE2469ayUS5Xf09ErHXpS6n1MCtj1rhAAsIFl8wWdvTKpaCxR/IjrzNikcoXlU00+t0v37QjOd2nq7wlr9/ZWuV2EmgAAAAAAAADUFkEnAACAtTQzIQ1+URo4Il0/U77ubZF6i92bug7QvQkA8KoVCkYXrk874+eKoaaTl5NK5wrL7nVZ0p6O4Pz4uf5IWPd2BuXzuOpQOQAAAAAAAIDNjqATAABAvRkjvfzjYvem56R8pvyeHQ9IBw9L+x+VAqG6lwgA2BiMMYrdmJ0PNA3G4joxktRUOlfV/l3bWtQ7H2qyta/LVpPPvcpVAwAAAAAAAEBlBJ0AAADqZfp6sXvT09L4ufJ1X6szlu7gYanrwbqXBwBofFcnU4oOz4WaEhoaSWhiukKgtoLucJP6IrZ6I7b6I2Ht77ZlN3lXuWIAAAAAAAAAqB5BJwAAgNVUKEiXfuSEm05/XSpky+/pOuCMptv/K5K/td4VAgAaVHwmo6GRhKKxhAaHnTF0Y8lUVXu3tfrUFwkXR9DZ6u0Oa3vQv8oVAwAAAAAAAMCrQ9AJAABgNUxdk45/Xjp2RJq4UL7uD0l975cOHJZ29NW/PgBAQ5lO53TycnK+U1M0FtfL4zNV7Q0GPMVAU1h93bb6esLqsgOyLGuVqwYAAAAAAACA2iLoBAAAUCuFgnTxB073phe/KRVy5fdEHnK6N+17r+RrqXOBAIBGkM7ldXp0UkMloaZzV6dUMMvvDXhd2t9lL+jWtHNri1wuQk0AAAAAAAAAGh9BJwAAgFdr8op0/HPSwBEp/nL5ut+W+j8oHTwsdeyrf30AgHUrly/opatTisac0XPRWEIvjiWVzS+favK6Le3tDKkvYqs/ElZvxNY97a3yuF11qBwAAAAAAAAA6o+gEwAAwEoU8tL570sDfyOd/Xbl7k09r3O6N93/bsnXXPcSAQDrS6FgdGl8WtFYQoOxuIZiCZ28nNRsNr/sXpcl3d3eqr5IWP3FMXR7dwTl97jrUDkAAAAAAAAArA8EnQAAAG5H8rL0wuelY5+REq+UrwfCUv+HnO5N7ffVvz4AwLpgjNHlRErRYWf83NCI07FpMlUhGFvBnVubF4Sa9nWF1OLnW3gAAAAAAAAAmxs/JQUAAFhOIS+d+6408LR09h8kU6Hzxp2vd7o33fcuyRuod4UAgDV2fSqtaCyuweGEorG4hkYSuj6VqWrvDjug3m5b/T1h9UVs9XbbCjf7VrliAAAAAAAAAGg8BJ0AAACWkohJL3xOOvZZKRkrX2/aIj3wYenAYWn7nvrXBwBYE4nZrE6MOOPnosVg0+VEqqq9bc3eBZ2a+iK22kMEZAEAAAAAAACgGgSdAAAASuVz0kvfkY4dca6mUH7PzjcWuzf9S8njr3uJAID6mcnkdOpyUoMxJ9AUjSV08fp0VXtb/R71dtvqKwk1RdqaZFnWKlcNAAAAAAAAABsTQScAAABJir/idG564bPS5Gj5evNW6YHHne5N2+6uf30AgFWXyRX04lhS0ZJQ09krkyqY5ff6PS7t6wrNB5r6ImHt2tYil4tQEwAAAAAAAADUCkEnAACweeWz0tlvSwNHpHPflVThnexdv+R0b7r3HZLHV9/6AACrJl8wOnd1aj7QFI3FdXp0Upl8hU5+i3hclu7tDJaEmmzt6QjK63bVoXIAAAAAAAAA2LwIOgEAgM3nxiXp2GekFz4nTV0pX2/ZLj34EenAR6Utu+peHgCgtowxenl8RtGRhKLDTrDpxOWEZjL5ZfdalrR7e6sTaOq21dcT1v07Qgp43XWoHAAAAAAAAABQiqATAADYHPJZ6cy3pIGnpfPfV3n3Jkva/bB08LC05210bwKABmWM0VgypcHhhIZG5ro1JZSYzVa1v2dLk/oiYfVHbPV2h7W/O6RgwLvKVQMAAAAAAAAAqkHQCQAAbGzj553uTcc/L01fK19v7Sx2b3pCattZ9/IAAK/OxHRGg7G4osVg02AsoWuT6ar2tgf986Gmvp6werttbWkh6AoAAAAAAAAA6xVBJwAAsPHkMtKL33C6N138YYUbLOmeX5YOHJb2/AvJTacOAGgEk6mshkYSxS5NTrem2I3ZqvaGm73q7bbVHwmrL2KrvyesjlBglSsGAAAAAAAAANQSQScAALBxXD8nHXtaOv5FaeZ6+Xqwy+nc9OBHpPAddS8PAFC9VDavk5eT84GmaCyuC9enZRZPHq2g2efW/m7b6dQUCas/ElbPliZZlrX6hQMAAAAAAAAAVg1BJwAA0Nhyaen0153uTZd+VL5uuaR7HpEOPind/cuSmy9/AGC9yeYLOjM2OR9oGowldPbKpPKF5VNNPo9L9+8IqW8+1GRr1/ZWuV2EmgAAAAAAAABgo+GdPgAA0JiunZWOHZGOf0GanShfD0WkAx91ujfZ3fWvDwBQUb5gdPH6lAaHb4aaTo0mlckVlt3rdlna0xFUX7etvh5nDN2ejqB8HlcdKgcAAAAAAAAArDWCTgAAoHFkZ6VTf+d0b3rlJ+Xrllva89Zi96Y3Sy53vSsEAJQwxmh4YlbREWf83OBwXCdGEprO5Kvav2t7i/ojYfV22+rvsXX/DltNPv5tBwAAAAAAAIDNiqATAABY/66elgaOSINflFLx8nX7DungR6UHPiKFdtS/PgCAJOlKMqXB4biGRhIajCU0FIvrxky2qr3d4Sb199jq7XbGz+2P2AoFvKtcMQAAAAAAAACgkRB0AgAA61NmRjr1rNO9afh/l6+7PNK9b3O6N+16E92bAKDObkxnFB1JKDocd66xuK4k01Xt3dbqV3/EVl8krL6Ird6IrW2t/lWuGAAAAAAAAADQ6Ag6AQCA9eXKSSfcNPi3UjpRvt62UzrwUemBx6VgZ72rA4BNaSqd04limCkaSygaS+iViZmq9oYCnvlAU18x3LTDDsiyrFWuGgAAAAAAAACw0RB0AgAAay8zLZ34qnTsiBT7Wfm6yyPtfafTvemuX5RcrrqXCACbRSqb1+nRpKKxhAZjcQ3FEjp3bUrGLL+3yevW/u5QSbAprJ1bmwk1AQAAAAAAAABqgqATAABYO6NRp3vT0FEpnSxf37JLOnBYeuDDUmt73csDgI0uly/o7JUpRWNxDcacjk1nxiaVKyyfavK6Ld23IzQfaOqPhLV7e4s8bsKoAAAAAAAAAIDVQdAJAADUV3pKOvEVJ+B0+Vj5ussr3f8up3vTnW+gexMA1EihYHRxfNoJNQ07oaaTl5NK5wrL7nVZ0j3tQSfU1BNWf8TWvZ1B+T3uOlQOAAAAAAAAAICDoBMAAKiPyy8Uuzc9I2Wmyte33u2Em/o/JLVsq3d1ALChGGM0Ep+dHz8XHU7oxEhCk+lcVfvv2tai3m5bfRFb/T1h7esKqdnHt48AAAAAAAAAgLXFT6oBAMDqSSWdsXTHjkijg+Xrbr90/7uL3Zt+XrKsupcIABvB1WRK0eLouehIQkOxhManM1Xt7bID6i0ZP9fbbctu9q5yxQAAAAAAAAAA3D6CTgAAoLaMkUaOSQN/44yoy86U37Pt3mL3pg9KzVvqXiIANLIb0xlFRxKKDt8MNY0lU1Xt3driU1/EVm/EGT/XG7HVHgyscsUAAAAAAAAAANQGQScAAFAbqYQU/bI0cES6MlS+7glI+94rHTgs3fE6ujcBQBUmU1kNFcNM0VhC0ZG4hidmq9ob9HvUWwwz9UfC6ovY6g43yeLfXwAAAAAAAABAgyLoBAAAVs4YKfYzaeBp6cRXpVyFN9/b73e6N/W9X2pqq3eFANAwZjN5nbycWDCC7sK16ar2Brwu7e9yxs/1RWz1RWzt3Noil4tQEwAAAAAAAABg4yDoBAAAbt/sjWL3pqelq6fK1z1N0v73OQGnyEN0bwKARdK5vF4cnSyOnosrGkvo7JVJFczye31ul+7bEVRfJKzeYqjp7u2t8rhdq184AAAAAAAAAABrqCGDTpZlRST9nqS3StoqaVTSs5I+ZYy5scIzf0HS9yW5JP2+MeZ3Fq3vlHTxFkf8rTHmg0ucfVjSr0m6X1Je0guS/tAY842V1AoAwJowRnrlp0646dSzUi5Vfk/Hfifc1PuY1BSud4UAsC7l8gW9dHXK6dJUHEH34lhS2fzyqSa3y9KejqD6um319djq6w7r3s6gfB5CTQAAAAAAAACAzafhgk6WZe2W9BNJ7ZKek/SipNdI+k1Jb7Us6/XGmPHbPDMo6YikGUmty9w+KCdUtdiJJc7+Q0m/LSkm6S8l+SR9UNLXLcv6dWPMf7udWgEAqLuZCWnwS07A6fqZ8nVvs7T/V6SDT0ndB+jeBGBTKxSMLlyfLgk1xXVqNKlUtrDsXsuSdm9vdUJNEVu9kbD2dYUU8LrrUDkAAAAAAAAAAOtfwwWdJP13OSGn3zDG/Oncg5Zl/ZGk35L0UekVHgAAIABJREFU+5L+7W2e+ceSbEl/UNx/K8eNMf+pmkMty/p5OSGn85Iemus2ZVnWf5E0IOkPLcv6hjHm0m3WCwDA6jJGevnH0sAR6dRzUj5dfk9nn3ToKWn/o1IgVP8aAWCNGWM0PDGrwVhcQyNOqOnESFJT6VxV++/c2qzeblv9xRF0+7pCCga8q1w1AAAAAAAAAACNq6GCTpZl7ZL0iKRLkv5s0fLvSvqEpCcsy/ptY8x0lWe+W9JTkp5Q7f8+5gJXv186Us8Yc8myrD+T9B+Lz/27NX5eAABWZnpcGvyCE3Aaf6l83dcq9T7qjKfrerDu5QHAWjHGaCyZmu/SFI0lNDSSUHwmW9X+Ljug3oitvkjY6dbUbSvc7FvlqgEAAAAAAAAA2FgaKugk6eHi9TvGmAWzH4wxk5Zl/VhOEOp1kr633GGWZbXLGSf3rDHmc5ZlPVlFDV2WZf0bSVsljUv6/4wx0WXq/XaFtb+XE3R6WASdAABryRjp0o+c0XSnvy7lM+X3dB2QDh52RtT5g3UvEQDq7fpUWkOxxM1g00hC1yYrdLerYFurT32RsNOtqcfW/m5b7cHAKlcMAAAAAAAAAMDG12hBp3uL17NLrL8kJ+i0R1UEnST9hSSXbm/U3S8XP+ZZlvUDSYeNMa+UPNYiqVvSlDFmdIlaVax1WZZlDSyxtLea/QAAlJm6Jh3/vHTsiDRxoXzdF5T63u8EnHb0178+AKiTxEzWGT03Eld02OnUNBKfrWqv3eSd79DUV+zYtMMOyLKsVa4aAAAAAAAAAIDNp9GCTnbxmlhife7x8HIHWZb1MUnvlvQBY8yVKp57RtL/I+lZSXPvBvdJ+k+S3iTpe5ZlPVAyMq9mtQIAUDOFgnTxh073phe/KRUqjFzqPuSMptv/PsnXUu8KAWBVTadzOjHihJkGYwkNxeK6ND5T1d4Wn1v7um31R2z1RsLqj9i6Y0szoSYAAAAAAAAAAOqk0YJOy5l7h8Hc8ibL2inpv0o6aoz5cjUHG2OuSvq/Fz38T5ZlPSLp/5X0Wkn/StIf30a9y9Za8vwHKz1e7PR04DafEwCw2UxekY5/Tjr2GenGpfJ1vy31f0A6cFjq3F/38gBgNaSyeZ0aTS4YQXfu2pRMFV+B+z0u3d8VUn/JCLq7trXK7SLUBAAAAAAAAADAWmm0oNNcFyR7ifXQovuW8teSZiX96qstyBiTsyzrr+QEnX5BN4NOy9W6XMcnAABenUJBuvCPTvemM38vFXLl9/S81unedP97JF9zvSsEgJrJ5gs6MzapaCyhoZG4BocTOntlUrnC8qkmj8vS3h1B9UXC6uu21RuxtacjKK/bVYfKAQAAAAAAAABAtRot6HSmeN2zxPo9xevZZc45ICdodG2JMROftCzrk5KeM8a8p4q6rhWv8/N9jDHTlmWNSOq2LGuHMWZ0hbUCAHB7kqPSC8XuTYlXytcDYan/Q9LBw1L7ffWvDwBepXzB6Py1KQ0Ox+dH0J0eTSqTKyy712VJ97QH1Ru5OYJub2dQAa+7DpUDAAAAAAAAAIBXo9GCTt8vXh+xLMtljJl/J8OyrKCk18vp1PTTZc75jKRKbSvukdOV6bikAUkvVFnX64rXC4se/0dJT0h6q6S/WbT2tpJ7AAB4dQp56dz3nO5NZ78tmXz5PXf8fLF707skb1O9KwSAFSkUjF6emFE0Fne6NcUSOnE5oZlMhX/nKti1rUW9Edvp1hSxta8rpGZfo30bBAAAAAAAAAAApAYLOhljzluW9R1Jj0j6NUl/WrL8KTkdlf7cGDM996BlWXuLe18sOec3Kp1vWdaTcoJO3zTG/M6itddKesEYk1n0+MOSfqv4x88tOvJ/ygk6fdKyrGeNMTeKe3YW60+rPAAFAED1EiM3uzclY+XrTW1S/4ed7k3b761/fQBwG4wxGonPaijmdGkaGnHCTZOpCqM3K4i0NalvLtTUbWtfty27ybvKVQMAAAAAAAAAgHppqKBT0a9K+omkP7Es682STkt6raQ3yRkD98lF958uXivOqLsN/1nSPsuyfiBp7p3kPkkPFz//j8aYn5RuMMb8xLKsP5L0HyRFLct6RpJP0gckbZH068aYS6+yLgDAZpPPSef+l9O96aXvSKbCqKadb3S6N+19p+QN1LtCAKjK1WRK0VjC6dY04nRrGp/OLL9RUnvQP9+lqS9iq7fb1tZW/ypXDAAAAAAAAAAA1lLDBZ2KXZ0OSfo9OSPh3i5pVNKfSPqUMWZilZ76s5LeK+khOWPnvJKuSPqypP9mjPnREvX+tmVZUUn/TtInJBUkHZP0X4wx31ilWgEAG1F8WHrhs9Kxz0qTl8vXm7dKD3xYOnBY2nZP/esDgFu4MZ0phpniTremWEJjyVRVe9uavSWhJufaESLECQAAAAAAAADAZtNwQSdJMsYMS3qqynur7uRkjHla0tNLrH1a0qerPWvR3iOSjqxkLwBgk8tnpbP/4HRvOvddSab8nrt+sdi96R2Sh24mANbeZCqroWKHpmgsoehIXMMTs1XtDfo92t9tq6/HVl+3E2qKtDXJsl5tg1YAAAAAAAAAANDoGjLoBADAhnfjktO56YXPSVNj5est26UHHpcOfFTaurvu5QHAnNlMXicvO4GmoZGEBmNxXbg2XdXegNel/V32ghF0O7e2yOUi1AQAAAAAAAAAAMoRdAIAYL3IZ6Uz35IGjkjn/1EVuzftftjp3rTnbZLHV+8KAWxy6VxeZ8Ymi6Pn4orGEjp7ZVKFCv9cLeZzu3TfjqD6ImH1FkNNd29vlcftWv3CAQAAAAAAAADAhkDQCQCAtTZxQTr2GemFz0vTV8vXWzukBz8iPfiEtOWu+tcHYFPK5Qt66eqUosVA09BIQqdHk8rml081uV2W9nQE1Vcygu7ezqB8HkJNAAAAAAAAAABg5Qg6AQCwFnIZ6cVvSMeOSBd+UOEGS7r7LdLBw9Ket0pub70rBLCJFApGF65Pa2gkrsFhJ9R08nJCqWxh2b2WJe3e3uqEmiK2eiNh7esKKeB116FyAAAAAAAAAACwmRB0AgCgnsbPSwNPS8e/IM1cL18P7nA6Nx14QgrfUffyAGx8xhgNT8wqOuJ0aorG4joxktRUOlfV/ju3Nqu321Z/cQTdvq6QggHCmAAAAAAAAAAAYPURdAIAYLXl0tLprzsBp0s/Kl+3XNI9j0gHDjtXN/89A6gNY4zGkqn5QNPcCLr4TLaq/V12QL0RW32RsNOtqdtWuNm3ylUDAAAAAAAAAABUxjupAACslmtnndF0x78gzU6Ur4e6pQMflR78iGRH6l8fgA3n+lRaQ7HEzWDTSELXJtNV7d3W6lNfJOx0a+qxtb/bVnswsMoVAwAAAAAAAAAAVI+gEwAAtZRNSaf/zune9PKPy9ctl7TnrdLBJ6W73yK53PWuEMAGkZjNOqGmkbiiw06nppH4bFV77SbvfIemuW5NO+yALMta5aoBAAAAAAAAAABWjqATAAC1cPW0NHBEGvyilIqXr9s9zmi6Bx+XQl31rw9AQ5tO53RixAkzzXVrujQ+U9XeFp9b+7ttJ9gUCas/YuuOLc2EmgAAAAAAAAAAQMMh6AQAwEplZ6WTzzrdm4Z/Wr5uuaV73yYdfEra/Sa6NwGoSiqb1+nRZDHQ5ISazl2bkjHL7/V7XLq/K6T+khF0d21rldtFqAkAAAAAAAAAADQ+gk4AANyuKyedcFP0b6VUonw9fKd08LD0wONSsLPu5QFoHNl8QWfGJhWNJTQ0EtfgcEJnr0wqV1g+1eRxWdq7I+iMnuu21RuxtacjKK/bVYfKAQAAAAAAAAAA6o+gEwAA1chMSye/5gScYj8rX3d5pL3vkA4+Kd31S5KLoAGAhfIFo/PXpjQ4HJ8fQXdqNKlMrrDsXpcl3dMeVG/EVn9xBN3ezqACXjrFAQAAAAAAAACAzYOgEwAAtzIalY4dkaJfltLJ8vW2u252b2ptr399ANYlY4wujc8oGos73ZpiCZ24nNBMJl/V/l3bWtQbsZ1uTRFb+7pCavbxpTsAAAAAAAAAANjceLcEAIDF0lPSia843ZsuHytfd3ml+/6l071p5xvp3gRscsYYjcRnNRRLKDqSUDQW11AsoWQqV9X+SFuT+uZCTd229nXbspu8q1w1AAAAAAAAAABA4yHoBADAnMvHnXDT0FEpM1W+vvVu6cBh6YEPSy3b6l4egPXh6mRK0eGFoabx6UxVe9uD/vkuTX0RW73dtra2+le5YgAAAAAAAAAAgI2BoBMAYHNLJaUTzzgBp9HB8nW3T7r/3U73pjtfL1lWvSsEsIZuTGcUHUloKBbXYHEE3VgyVdXetmZvSajJuXaEAqtcMQAAAAAAAAAAwMZF0AkAsPkY44ykG3haGvqKlJ0uv2fbHifc1PdBqWVrvSsEsAYmU1kNjSQWjKAbnpitam/Q71FvxFZvxFZftxNqirQ1ySIcCQAAAAAAAAAAUDMEnQAAm0cqIUW/LA0cka4Mla+7/dK+9zoBpzteR/cmYAObzeR1ajShweGEhkYSGozFdeFahdBjBU1et/Z1hRaMoNu5tUUuF/9mAAAAAAAAAAAArCaCTgCAjc0YKfa8073p5Fel7Ez5PdvvK3Zver/UvKXeFQJYZelcXmfGJouj5+KKxhI6e2VSBbP8Xp/bpft2BNUXCas3Yqs/Etbu7S3yuF2rXzgAAAAAAAAAAAAWIOgEANiYZuPF7k1PS1dPlq97AtK+9zkBp57X0L0J2CBy+YJeujqloZjTpWloJKEXRyeVyReW3et2WdrTEVR/yQi6ezuD8nkINQEAAAAAAAAAAKwHBJ0AABvHXPem5z8tnfyalEuV39O+Tzr0lNT7mNQUrn+NAGqmUDC6cH1aQyPx+RF0Jy8nlMouH2qyLGn39lb1dTuj53ojYe3rCingddehcgAAAAAAAAAAAKwEQScAQOPLTEtDz0g/+ytpLFq+7m2W9r9POviU1H2Q7k1AAzLGaHhiVtERZ/RcNBbXiZGkptK5qvbfubVZvd3O6LneiK19XSEFA95VrhoAAAAAAAAAAAC1RNAJANC4rr8k/ezT0vEvSOlE+XpnrxNu6n1MCoTqXx+AFTHGaCyZUjSWWDCCLj6TrWp/lx1wRs9Fwk63pm5b4WbfKlcNAAAAAAAAAACA1UbQCQDQWPI56cy3nO5NF39Yvu4JSPsflR76mNR1gO5NQAO4PpXWUCwx36kpOpLQtcl0VXu3tfrUFwk73Zp6bO3vttUeDKxyxQAAAAAAAAAAAFgLBJ0AAI0hOSodOyINPC1Njpavb9klHfq49MCHpeYtdS8PQHUSs1kn1DQSnw83jcRnq9prN3nnOzTNdWvaYQdkEWgEAAAAAAAAAADYFAg6AQDWL2OkSz9yujed/oZk8gvXLZd079ulhz4u3fVLksu1JmUCqGw6ndPJy0mnS1OxW9Ol8Zmq9rb43NrfbTvBpkhY/RFbd2xpJtQEAAAAAAAAAACwiRF0AgCsP6mENPglJ+B0/Wz5eku7dPCwdPBJyY7UvTwAS3vpyqSODsT0gzNXde7qlApm+T1+j0v3d4XUXzKC7q5trXK7CDUBAAAAAAAAAADgJoJOAID1YzTqhJuGjkrZCl1f7nyD071p7zslj6/+9QGoKDGb1d8NXtYzAzENDsdvea/HZWnvjqAzeq44gu6ejlZ53XRkAwAAAAAAAAAAwK0RdAIArK1sSjr1nBNwiv1z+bovKPV/0Ak4td9X//oAVJQvGP343HUdHYjpH06OKZMrlN3jsqR72oPqi9wcQbe3M6iA170GFQMAAAAAAAAAAKDREXQCAKyNG5ek5/9aOvZZaXaifL19nxNu6nu/5A/WvTwAlV26Pq1nBmL6yrGYRhOpsnWf26W33N+uRw9G9LpdW9Xs48tNAAAAAAAAAAAA1AbvPAEA6qeQl8591+ne9NL/kmQWrru80r73SIc+Lt3xOsmy1qRMAAtNp3P65tConnk+pn++VCGYKGl/d0iPHezRu/q71NbCaEkAAAAAAAAAAADUHkEnAMDqm74uvfBZp4NT/JXydbtHOvSU9OATUmt7/esDUMYYo3++OKGjAzF9a2hUM5l82T1bWnx6zwPdeuxQRPftCK1BlQAAAAAAAAAAANhMCDoBAFaHMdLwPzvdm049K+Uz5ffc/RbpoX8l3fOI5HLXv0YAZUbis/rKQEzPDMT0ysRM2brbZelN927Xowd79PDedvk8rjWoEgAAAAAAAAAAAJsRQScAQG2lp6Sho9LPPi1dGSpfb2qTHvyIdOhj0pZd9a8PQJlUNq9/ODmmo8/H9OPz12VM+T33tLfqsUMRvefBbrUHA/UvEgAAAAAAAAAAAJseQScAQG1cO+OEmwa/KKWT5evdh5zuTfveI3mb6l8fgAWMMTo+HNfRgZi+PnhZk6lc2T3BgEfv6u/SY4d61B+xZVnWGlQKAAAAAAAAAAAAOAg6AQBWLp+VXvyGE3C69KPydU+T1Puo9NDHpa4H618fgDJXJ1P62rERHR2I6dzVqbJ1y5LecPc2PXaoR4/c36GAl7GSAAAAAAAAAAAAWB8IOgEAbl9iRDp2RBo4Ik2Nla9vvVs69HHpgQ85o+oArKlMrqB/fPGKjj4f0w/OXlO+UD6bbufWZj16MKL3HYioK0zXNQAAAAAAAAAAAKw/BJ0AANUxRrr4Q+lnfyW9+C3J5BeuW25p79ud8XR3/aLTFgbAmjp5OaGjz8f03PER3ZjJlq03+9x6R+8OPXaoRw/tbGM0HQAAAAAAAAAAANY1gk4AgFubjUuDX3TG042/VL7e2ikdPCwdOCzZ3fWvD8ACE9MZPXd8REefj+nUaLLiPa+5a4seOxjR23t3qMXPl4MAAAAAAAAAAABoDLyzBQCo7PJxp3vT0DNSbrZ8fecbne5Ne98hub31rw/AvFy+oH966ZqOPh/Td09fUTZfPpquyw7oVw5G9OjBiO7c2rIGVQIAAAAAAAAAAACvDkEnAMBN2ZR08mtOwGnk+fJ1f0jq/5B06GNS+9761wdggXNXp3R0YFhfOzaiq5PpsnW/x6W37u/UYwd79HO7t8rtYjQdAAAAAAAAAAAAGhdBJwCANHFBev5vpBc+J81OlK939EoPfVzqfUzyt9a/PgDzkqmsvjE4qqMDw3rhlXjFex7oCeuxQxG9s69LdhMd1wAAAAAAAAAAALAxEHQCgM2qkJde+o7Tvenc9yQtGnXl9kn73uuMp4s8JFl0ggHWSqFg9JPz4zo6MKxvnxhTOlcou2d70K/3PditRw9GdE9HcA2qBAAAAAAAAAAAAFYXQScA2GymrkkvfMbp4JQYLl8P3+GMpnvwCallW/3rAzDvlfEZPTMwrK8cG9FIfLZs3eu29Oa9HXrsUES/uGe7PG7XGlQJAAAAAAAAAAAA1AdBJwDYDIyRXvmp073p1HPS/8/enUfnXdb5/39eWZp0TVva0jYJlC50YWlpAoKIbMouMNCIDosgzDg6o47j6FeFccYFHWecceGnsyirjAIpCEjZlwqyDCSFFuhKN5Lu+95mu35/3CFTcqcrn9537uT5OKfn7nW/r8/neus5bTknr/O+WhrbbQgw5uOp6U2jPwZ5+VlpUxJsb2ji0TdXUl1Tx/8u7uAqSWD8sH5UVZRx6QmlDOzdI8MdSpIkSZIkSZIkSdlh0EmSurJdW2DWffDarbD67fR6z4Ew+RqovA4GjMh4e5JSYozULN1AdU0d02atYFtDc9qeAb0KuWRSKVWVZRwzvCQLXUqSJEmSJEmSJEnZZdBJkrqi1XNS4aaZ90DDlvR62Ump6U0TLoHC4sz3JwmAFZt2cH9tPVNr61mybntaPS/AGWOHUFVRxlnjh1BU4LQ1SZIkSZIkSZIkdV8GnSSpq2hqgLl/gNdug6V/Sq8X9oLjquDE62HYxMz3JwmAnY3NPDl7FdU1dfzpnbXEmL5n1ODeVFWWc9kJpQzpZxhRkiRJkiRJkiRJAoNOkpT7NtVD7R1QeydsW51eH3Q0VF4PEz8FPftnvD1JqavpZtVvorq2joffWM7mnU1pe/oWFXDRxOFUVZZxQnl/QghZ6FSSJEmSJEmSJEnqvAw6SVIuammBxdNT19PNexRiy/vrIR/GX5S6nm7EaWBgQsqKNVt28eDry6iurWP+qq1p9RDg1FGDqKos45wJQ+nZw6vpJEmSJEmSJEmSpD0x6CRJuWTHBnjjt6mA0/qF6fW+w6DiWpj8Geg3LOPtSYKGphaem7ea6pp6npu3muaW9LvpjhjYiykVZVw2uZSyAb2y0KUkSZIkSZIkSZKUeww6SVIuWDYjFW56ayo07UyvH3V6anrT2PMhvzDz/UlizorNVNfU89Aby1i3rSGt3rMwnwuOG0ZVZRknjRhIXp6T1iRJkiRJkiRJkqQDYdBJkjqrxh3w1gPw2q9h+Yz0elEJTPpzqPwsDD468/1JYuP2Bh56YznVtXW8tWxzh3tOHDGAqopyLjh+GH2K/E8vSZIkSZIkSZIk6WD50zZJ6mzWLYSa2+D1u2HnxvT60ONT05uOmwI9eme+P6mba26JPL9gDVNr6nlq9ioamlvS9gwrKeayyaVMqSjnqEH+OZUkSZIkSZIkSZKSYNBJkjqD5iZY8ERqetPCZ9Pr+UVw7GWpgFNpBQSvvJIybeGarUytreeBGfWs2rwrrd6jII9zjxlKVUUZp44eRL5X00mSJEmSJEmSJEmJMugkSdm0dTXMuBNq7oDN9en1/kfCidfDpKug92EZb0/q7rbsbOSRWSuorqljxrsdTFgDJpaVMKWynIuPH05Jr8IMdyhJkiRJkiRJkiR1HwadJCnTYoR3X05Nb5r9MLQ0ttsQ4OhzU9ObRp0NeXlZaVPqrlpaIq8sWkd1bT2PvbWCnY3pV9MN6tODPzshdTXd2KF9s9ClJEmSJEmSJEmS1P0YdJKkTNm5GWbdC6/dCmvmpNd7DYLJ10DFtTDgyIy3J3V3deu3M7W2nvtn1FO/YUdavSAvcNa4IVRVlnPG2MEU5htClCRJkiRJkiRJkjLJoJMkHWqr3k6Fm2bdCw1b0+vlJ6emN024GAqKMt+f1I3taGjmsbdWUF1Tz8uL1nW4Z9zQvlRVlnPJpOEM6uOfUUmSJEmSJEmSJClbDDpJ0qHQ1ABzHk5dT/fuy+n1wt5w/CfhxOth6HGZ70/qxmKM1C7dwNTaeh6ZtYKtu5rS9pT0LOTSScOpqiznmOH9CCFkoVNJkiRJkiRJkiRJuzPoJElJ2lgHtbfDjLtg25r0+uBxUHk9TLwCiksy35/Uja3ctJP7Z9Rzf209i9ZuS6vnBfjo0YOpqijnYxOGUFSQn4UuJUmSJEmSJEmSJO2JQSdJ+qBaWmDRs6nr6eY/DrHl/fW8Ahj/idT1dEeeCk6GkTJmV1MzT81eRXVNPS8sWENLTN8zclBvplSWcdkJZQwtKc58k5IkSZIkSZIkSZL2i0EnSTpY29fD63dDzW2wYXF6vV8pVFwHk6+GvkMz35/UTcUYeWvZZqpr63jojeVs2tGYtqdPUQGfmDiMKRVlTD5igFfTSZIkSZIkSZIkSTnAoJMkHYgYYdkMeO3X8Nb90Lwrfc/IM1PTm44+D/L9a1bKlLVbd/Hg68uYWlvP3JVbOtzz4VGHUVVZxnnHDKNnD6+mkyRJkiRJkiRJknKJP4GXpP3RsD0VbHrt17DijfR6cQlMugoqPwuDRme+P6mbamxu4bm5q6muree5uatp6uBuurIBPZlSUcblk8soH9grC11KkiRJkiRJkiRJSoJBJ0nam7XvpK6me+Nu2LkpvT5sUmp607GXQw8DFFKmzFu5heqaOh58Yxlrtzak1YsL87jg2GFMqSzj5KMOIy/Pq+kkSZIkSZIkSZKkXGfQSZLaa26C+Y+lpjctmp5eLyhOBZtOvB5KKzLentRdbdreyMMzl1FdW8+s+g6Ch0DFkQOoqijjwuOH0be4MMMdSpIkSZIkSZIkSTqUDDpJ0nu2rIQZd0HN7bBleXp9wFGpcNOkK6HXwMz3J3VDzS2RP72zluqaOp6cvYqGppa0PYf3K+KyyWVMqShj1OA+WehSkiRJkiRJkiRJUiYYdJLUvcUIS19MTW+a8wdoaXp/PeTB0eelAk4jz4K8vOz0KXUzi9duY2ptHQ/MWMaKTTvT6j3y8/j4MYdTVVHGaWMGk+/VdJIkSZIkSZIkSVKXZ9BJUve0cxPMvBdqboU1c9PrvQfD5M9AxbXQvzzj7Und0dZdTUybtZzqmnpqlm7ocM9xpSVUVZZx8cTh9O/VI8MdSpIkSZIkSZIkScomg06SupeVb8Jrt8Ks+6BxW3r9iA+npjeNvxgKDFFIh1pLS+R/F6+nuraOx95cyY7G5rQ9h/XuwaUnlFJVWca4of2y0KUkSZIkSZIkSZKkzsCgk6Sur2kXzH4oFXCqeyW93qMPHH9FKuB0+DGZ70/qhuo3bOf+2mVMnVFH3fodafX8vMCZY4dQVVnGmWOH0KPAayMlSZIkSZIkSZKk7s6gk6Sua8NSqL0dZvwGtq9Nrw8eDyfdkAo5FfXNfH9SN7OjoZkn3l5JdW0dLy1cR4zpe8Ye3peqyjIumVTK4L5FmW9SkiRJkiRJkiRJUqdl0ElS19LSAgufgdd+DfOfANolKfIKYcLFcOINcMQpEEJW2pS6ixgjr9dtpLqmnkdmLmfLrqa0Pf2KC7hkUupquuNKSwj+uZQkSZIkSZIkSZLUgZwMOoUQyoDvAucBhwErgAeB78QYNxzkOz8KPAfkATfHGG9qVx8DXAacC4wBDgc2AK8AP40xPtfBO68Fbt/LsZ+PMf7nwfQrqZ1t6+D130DNbbBxaXq9XxlUXgeTr4E+QzLfn9TNrNq8kwdmLGNqbR0L12xLq4cAp40ZTFVFGR+fcDjFhflZ6FKSJElLMOs/AAAgAElEQVSSJEmSJElSLsm5oFMIYRTwEjAEeAiYC5wEfBk4L4Rwaoxx3QG+sy9wJ7Ad6LOHbd8DrgBmA48C64GxwMXAxSGEL8cYf76HZx8C3ujg+5oD6VNSOzFCfU1qetPbv4fmXel7Rp2dmt405hzIz7m/8qScsqupmWfmrKa6po4/zl9DSwdX0404rBdVleVcNrmUYSU9M9+kJEmSJEmSJEmSpJyViz/1/yWpkNOXYoy3vPdlCOHfga8ANwN/dYDv/BlQAvyw9fmOPA78KMb4+u5fhhBOB54C/jWEUB1jXNHBsw/GGO84wJ4k7UnDNnhzairgtHJWer24P5xwFVR+Fg4blfn+pG7mrWWbmFpbz4NvLGPj9sa0eu8e+Vx4/DCqKsupPHKAV9NJkiRJkiRJkiRJOig5FXQKIYwEzgGWAL9oV/5H4C+Bq0MIX40xpt+T0/E7LwGuA65mL/9/7CmoFGP8YwhhOvBx4MPA/ftzrqSDsGZ+6mq6N34Luzal10sroPJ6OPYyKHRSjHQord/WwIOvL6O6tp45KzZ3uOfkkQOpqijn/OOG0qtHTv0nhyRJkiRJkiRJkqROKNd+6nhW6+eTMcaW3Qsxxi0hhBdJBaFOBp7Z18tCCEOAX5GauHR3COHag+zrvfEVTXuoTwoh/C1QDCwDnosx1h/kWVL30twI8x5NTW9a/Hx6vaAYjpuSCjiVTs58f1I30tTcwvR5a5haW88zc1fR2Jx+N11p/55cXlHGlMllHHFYryx0KUmSJEmSJEmSJKmryrWg09jWz/l7qC8gFXQ6mv0IOgH/DeRx4FfdtQkhHAmcDWwHOkhhAPDlduvmEMKvgb+NMe7cz3Nq91Aat1+NSrlm8wqYcSfU3gFbOrgRcuAoOPF6mPhp6DUw4+1J3cmCVVuorq3ngRnLWLt1V1q9qCCP848dSlVlOaeMPIy8PK+mkyRJkiRJkiRJkpS8XAs6lbR+dnBn1fu+77+vF4UQPgtcAlwRY1x1MM2EEIqA/wGKgK/HGDe027IY+CLwJFBPqv+PAD8EPgf0A/78YM6WuqQYYckLqelNcx6B2Pz+esiDsRekAk5HnQF5eVlpU+oONu1o5A8zl1NdW8/Muo0d7jnhiP5UVZRz0cRh9CsuzHCHkiRJkiRJkiRJkrqbXAs67ct7IyTS79LZfVMII4CfAtUxxvsO6qAQ8oHfAKcC9wI/br8nxvhH4I+7fbUdqA4hvALMBD4dQvhRjHHmvs6LMVbsoY9awPu6lNt2bISZ90DNrbC2g4FtvYdAxbVQ8RkoKct4e1J30dwSeWnhWqpr6nni7ZXsampJ2zO4bxGXTS6lqqKM0UP6ZqFLSZIkSZIkSZIkSd1VrgWd3pvYVLKHer92+/bkNmAH8IWDaaI15HQ3UAXcB1wVY9xruGp3Mca6EMKjwJXAR0mFnqTuZ8Ws1PSmN6uhcXt6/ciPpKY3jbsICnpkvj+pm1i6bhtTa+u5v7ae5ZvSb1QtzA98fMLhVFWUc9qYQRTkO01NkiRJkiRJkiRJUublWtBpXuvn0Xuoj2n97GAkzPtMJhWWWhNC6Kh+YwjhRuChGOOluxdCCAXAb0mFnH4LXBNj+/u19sua1s/eB/GslLsad8Lsh1IBp/pX0+s9+sLET6UCTkPGZ74/qZvYtquJaW+uYGptPa8uXt/hnmOG96OqooxLJpUyoLdhQ0mSJEmSJEmSJEnZlWtBp+daP88JIeTFGNvu1Akh9CV1jdwO4JV9vOcuoFcH348hNWHpDaAWeH33YgihB6kJTpe0vuO63Xs4QB9q/Vx0kM9LuWXDEqi5DWb8BnZ0EKoYcgycdAMcVwVFXoclHQoxRl5dvJ7q2noefXMF2xvSc7oDe/fgkknDqaooZ8Lwfh28RZIkSZIkSZIkSZKyI6eCTjHGhSGEJ4FzgL8Gbtmt/B1S05H+K8a47b0vQwjjWp+du9t7vtTR+0MI15IKOk2LMd7UrlYEPABcANwK/OW+Qk4hhNNijC+0+y4A3wBOAdYCj+/tHVJOa2mGd55OTW9a8BTQ7obHvEI45lI48QYo/xB0PGFN0ge0fOMO7q+tZ+qMepauS78mMj8vcMbRg6mqLOOscYfTo8Cr6SRJkiRJkiRJkiR1PjkVdGr1BeAl4OchhLOBOaSmI51J6sq6G9vtn9P6+UETFP9JKuS0FlgGfLuDa++mxxin77Z+PoQwH3it9ZkSUlOnjgW2A1fGGDd/wL6kzmfbWphxF9TeDhvfTa+XHAGV18EJV0OfwZnvT+oGdjY288TbK5laW8+f3llLjOl7Rg/pQ1VFGX82uZQhfYsz36QkSZIkSZIkSZIkHYCcCzq1TnWqBL4LnEcqfLQC+DnwnRhjB3diJeKo1s9BwLf3sm/6br//MXAScBYwEGgB3gV+Afx7jNFr69R1xAh1r6amN81+EJob2m0IMPpjqelNYz4OeflZaVPqymKMzKzfRHVNHQ/PXM6WnU1pe/oWF3DxxOFMqShjUnl/OgjtSpIkSZIkSZIkSVKnlHNBJ4AYYx1w3X7u3e+f4MYY7wDu2EPtjP19z27PfO1An5Fyzq6t8GY1vHYrrHozvd5zQGpyU+V1MHBk5vuTuoHVW3by+xnLmFpbz4LVW9PqIcBHRg9iSkUZ5x4zlOJCg4aSJEmSJEmSJEmSck9OBp0kdQKr50LNrTDzHtjVwQ2MZSempjdNuBQKvRJLSlpDUwvPzl1FdU090+evobkl/W66Iw/rxZTJZVxWUUZp/55Z6FKSJEmSJEmSJEmSkmPQSdL+a26EuY+kpjcteSG9XtATjq+Cyuth+KTM9yd1A7OXb6a6to6H3ljO+m3tr4iEXj3yueC4YVRVlHHSUQO9mk6SJEmSJEmSJElSl2HQSdK+bVoGM+6E2jth68r0+mGjU9ObJn4aevbPfH9SF7dhWwMPvbGM6tp63l7ewQQ14KSjBjKloowLjxtG7yL/eZckSZIkSZIkSZLU9fiTUEkdixEWTU9dTzf3UYjN76+HfBh3IZx4PRx1Ojg1RkpUU3MLLyxYS3VtHU/PXk1Dc0vanuElxVxeUcblk8sYMah3FrqUJEmSJEmSJEmSpMwx6CTp/XZsgDd+lwo4rXsnvd5nKFRcCxWfgX7DM96e1NW9s3or1bV1/H7GMlZv2ZVWLyrI49xjhlJVWcaHRw0iP8+QoSRJkiRJkiRJkqTuwaCTpJTlb8Brv4Y3p0LTjvT6iNNS19ONuxDyCzPfn9SFbd7ZyCMzV1BdW8fr727scM/E8v5UVZTxiYnDKenpn0FJkiRJkiRJkiRJ3Y9BJ6k7a9wJb/8+FXBaVpNeL+oHEz+dup5u8NjM9yd1YS0tkZcXraO6po7H317Jzsb0q+kG9SnissmlVFWUMebwvlnoUpIkSZIkSZIkSZI6D4NOUne0fhHU3Aav3526qq69w4+Dk26AY6dAUZ/M9yd1YXXrt1NdW8/9tfUs25g+Pa0wP3D2uMOpqizjo0cPpjA/LwtdSpIkSZIkSZIkSVLnY9BJ6i5ammHBk6npTe88nV7P7wHH/FnqerqyEyGEzPcodVHbG5p49M2VTK2t45VF6zvcM35YP6oqyrhk0nAO61OU4Q4lSZIkSZIkSZIkqfMz6CR1dVvXwOt3Qc3tsKkuvd7/CKi8Hk64CnoPynx/UhcVY6Rm6Qaqa+qYNmsF2xqa0/b071XIpZNKmVJRxrGlJVnoUpIkSZIkSZIkSZJyh0EnqSuKEd59JTW9afZD0NLYbkOAMeekpjeNPhvy8rPSptQVrdi0gwdmLGNqbT2L125Lq+cFOP3owVRVlnP2+CEUFfjnT5IkSZIkSZIkSZL2h0EnqSvZtQVm3Qev3Qqr306v9zoMTrgaKq+DASMy3p7UVe1sbOap2auorq3nTwvW0BLT94wc3JuqinIum1zK4f2KM9+kJEmSJEmSJEmSJOU4g05SV7BqNtTcCjPvhYYt6fXyD6WmN024BAqKMt+f1AXFGHlz2Saqa+p56I1lbN7ZlLanb1EBF00cTlVlGSeU9yeEkIVOJUmSJEmSJEmSJKlrMOgk5aqmBpj7h9T0pqUvptcLe8Hxn4TK62HY8ZnvT+qi1mzZxYOvp66mm7eqg2AhcOrow6iqKOfcY4bSs4dX00mSJEmSJEmSJElSEgw6SblmUz3U3gG1d8K21en1QUenpjdN/BQUl2S8Pakramxu4dm5q6muqWf6vNU0dXA3XfnAnkyZXM7lFaWUDeiVhS4lSZIkSZIkSZIkqWsz6CTlgpYWWPRcanrT/Mcgtry/nlcA4y5KBZxGfAS8HktKxNyVm6muqefB15exbltDWr1nYT7nHzeUqopyPnTUQPLy/LMnSZIkSZIkSZIkSYeKQSepM9u+Ht74LdTcCusXpdf7DoOK62DyNdBvWOb7k7qgjdsbeHjmcqpr6nlz2aYO95w4YgBVFeVccPww+hT5T6kkSZIkSZIkSZIkZYI/nZU6o2UzUtOb3poKTTvT60ednpreNPZ8yC/MfH9SF9PcEnl+wRqm1tbz1NuraGhuSdsztF8xl1eUMqWinKMG9c5Cl5IkSZIkSZIkSZLUvRl0kjqLxh3w1v2pgNPyGen1ohI44Uqo/CwMGpP5/qQuaNGarVTX1vPAjHpWbd6VVu9RkMc5Ew6nqrKcj4weRL5X00mSJEmSJEmSJElS1hh0krJt3UKouQ1evxt2bkyvDz0eTvoLOPZy6OEUGemD2rKzkWmzVlBdW0/t0g0d7jm+rISqijIunlhKSS+npkmSJEmSJEmSJElSZ2DQScqG5iZY8AS89mtY+Gx6Pb8Ijr0sdT1daQUEp8hIH0RLS+SVxeuYWlPPo2+tYGdj+tV0g/r04NJJpVRVljN2aN8sdClJkiRJkiRJkiRJ2huDTlImbVkFr98FNXfA5vr0+oARUHk9nHAV9BqY6e6kLqdu/Xbun1HP1Np66jfsSKsX5AXOGjeEqspyzhg7mML8vCx0KUmSJEmSJEmSJEnaHwadpEMtRlj6Ump605yHoaWp3YYAR5+Xmt406izIM2ghfVCPv7WCu15eyksL13VYHze0L1Mqyrj0hFIG9SnKcHeSJEmSJEmSJEmSpINh0Ek6VHZuhln3wmu3wpo56fVeg2DyNVBxLQw4MuPtSV3VHS8u5p/+MDvt+5KehVwyaThVFeUcW9qP4JWQkiRJkiRJkiRJkpRTDDpJSVv1dircNOteaNiaXj/ilNT0pvGfgAInyUhJWrt1Fz9+cn7bOi/AaWMGU1VZxsfGH05xYX4Wu5MkSZIkSZIkSZIkfRAGnaQkzXscfndF+veFvWHiFVB5PQw9NvN9Sd3ET56az9ZdqeshRw3uzf/ccDJDS4qz3JUkSZIkSZIkSZIkKQkGnaQkjTwdeg6AHRtS68HjUtObjr8Civtltzepi5u/agu/e/XdtvVNF04w5CRJkiRJkiRJkiRJXYhBJylJhT2h4jrYsCQVcDrywxBCtruSuoWbp82hJaZ+f9qYQZwxdnB2G5IkSZIkSZIkSZIkJcqgk5S0s79tuEnKsOnzVvPH+WsAyAtw44XjCf45lCRJkiRJkiRJkqQuJS/bDUhdjuEKKaOamlu4edqctvUVJ5YzbqhXRUqSJEmSJEmSJElSV2PQSZKU0+55rY4Fq7cC0LtHPn/38bFZ7kiSJEmSJEmSJEmSdCgYdJIk5azNOxv5yVPz29ZfOHM0g/sWZbEjSZIkSZIkSZIkSdKhYtBJkpSzfvHcO6zb1gBAaf+eXP+Ro7LckSRJkiRJkiRJkiTpUDHoJEnKSXXrt3P7n5a0rf/f+eMoLszPXkOSJEmSJEmSJEmSpEPKoJMkKSf982NzaWhuAeCEI/rzieOHZbkjSZIkSZIkSZIkSdKhZNBJkpRzapasZ9qbK9rWN104gRBCFjuSJEmSJEmSJEmSJB1qiQadQgiFSb5PkqT2Wloi35s2p239iYnDqThyQBY7kiRJkiRJkiRJkiRlQtITnZaFEH4UQhid8HslSQLg4ZnLmVm3EYAeBXl8/dyxWe5IkiRJkiRJkiRJkpQJSQed8oCvAfNCCE+FEC4PIRQkfIYkqZva0dDMjx6f27a+/iNHUT6wVxY7kiRJkiRJkiRJkiRlStJBp+HAVcALwNnAfUBdCOHmEMJRCZ8lSepmbv3TIlZs2gnAoD49+MIZo7LckSRJkiRJkiRJkiQpUxINOsUYG2KMv40xngGMA34KFADfBBaEEB4NIVwSQkg6YCVJ6uJWb97JL6cvbFv/3cfH0re4MIsdSZIkSZIkSZIkSZIy6ZAFjmKM82OMXwVK+b8pT+cBDwDvhhD+KYQw/FCdL0nqWv7tyflsb2gGYOzhfflkZVmWO5IkSZIkSZIkSZIkZdIhn6wUY2wApgG/B5YDgdQVd98GFocQfhpCKDrUfUiSctfs5Zu5r7aubX3TReMpyHc4oCRJkiRJkiRJkiR1J4f0p8QhhJNDCLeTCjj9BOgN/ByYBHwWmAd8kdQVd5IkpYkx8v1ps4kxtT5z7GBOGzM4u01JkiRJkiRJkiRJkjKuIOkXhhD6AlcDnwOOJTXBaQbwH8BvY4w7WrfOCiH8BngcmAJ8PuleJEm575k5q3lp4ToA8vMCN144PssdSZIkSZIkSZIkSZKyIdGgUwjh18AVQC9gF/Ab4Jcxxlc72h9jbA4hTAfOSrIPSVLX0Njcwg8endO2vvJDRzB6SN8sdiRJkiRJkiRJkiRJypakJzp9FlgI/Cdwe4xx/X48Mx34bsJ9SJK6gLtfWcqitdsA6FtcwJfPHpPljiRJkiRJkiRJkiRJ2ZJ00On8GOMTB/JAjPFF4MWE+5Ak5biN2xv46dML2tZfPGs0h/UpymJHkiRJkiRJkiRJkqRsykvyZQcacpIkaU9uefYdNu1oBOCIgb34zIdHZLchSZIkSZIkSZIkSVJWJRp0CiGcHUK4LYQwfA/14a31M5I8V5LUtSxeu427Xl7Stv7m+eMoKsjPWj+SJEmSJEmSJEmSpOxL+uq6LwLjYozLOyrGGJeHEE4BSoDpCZ8tSeoifvjoHBqbIwAnjhjAeccOzXJHkiRJkiRJkiRJkqRsS3SiEzAZeGkfe/4EVCZ8riSpi3h54TqenL2qbX3ThRMIIWSxI0mSJEmSJEmSJElSZ5B00GkI0OE0p92sat0nSdL7tLREvj9tdtv6shNKmVjeP4sdSZIkSZIkSZIkSZI6i6SDTpuA8n3sKQe2JXyuJKkLuH9GPW8v3wxAcWEef3/u2Cx3JEmSJEmSJEmSJEnqLJIOOr0KXBpCGNpRMYQwHLi0dZ8kSW22NzTxr0/Ma1v/5WkjGd6/ZxY7kiRJkiRJkiRJkiR1JkkHnW4B+gIvhBAuDiEUAYQQikIIlwDPA32Anyd8riQpx/3XHxexessuAIb0LeJzp4/KckeSJEmSJEmSJEmSpM6kIMmXxRifDCF8D/gH4PdADCFsAAYAofXXd2OMjyd5riQpt63YtIP/en5h2/rvzx1L76JE/4mSJEmSJEmSJEmSJOW4pCc6EWP8R+A84FFgPVDS+jkNODfG+E9JnylJym3/+sQ8dja2ADBhWD8un1yW5Y4kSZIkSZIkSZIkSZ3NIRmXEWN8EnjyULxbktS1zKrfyAMzlrWtb7poPPl5IYsdSZIkSZIkSZIkSZI6o8QnOkmStL9ijHz/kTlt649POJwPjxqUxY4kSZIkSZIkSZIkSZ2VQSdJUtY88fZKXl2yHoCCvMA3zx+X5Y4kSZIkSZIkSZIkSZ1V4kGnEMKwEMIvQgjvhBB2hBCaO/jVlPS5kqTcsqupmR8+Nrdtfc0pIxg5uE8WO5IkSZIkSZIkSZIkdWYFSb4shFAKvAocDrwNFAFLgV3AyNbz3gA2JXmuJCn33PXSUpau2w5ASc9CvnT26Cx3JEmSJEmSJEmSJEnqzJKe6PRtYChwXoxxYut3t8cYx5EKOj0B9AQuS/hcSVIOWb+tgZ8/u6Bt/eWzx9C/V48sdiRJkiRJkiRJkiRJ6uySDjqdCzweY3y6fSHGWA9UkQo6fSfhcyVJOeRnT89ny87ULaYjB/Xm6lOOzHJHkiRJkiRJkiRJkqTOLumg01BSV9a9p5lUsAmAGONW4CngkoTPlSTliHdWb+Hu/323bf3NC8ZTmJ/0P0eSJEmSJEmSJEmSpK4m6Z8sbwZ2v3toA1Dabs8mYHDC50qScsQPHp1Lc0sE4JSRh/Gx8UOy3JEkSZIkSZIkSZIkKRckHXRaCpTvtp4JnBVC6AUQQsgDzgHqEz5XkpQDXliwhmfnrgYgBLjpovGEELLclSRJkiRJkiRJkiQpFyQddHoGODOEUNi6vhMYDrwUQvhX4EXgGODehM+VJHVyzS2Rm6fNaVtXVZRxzPCSLHYkSZIkSZIkSZIkScolBQm/71ZS19UNAlbEGO8OIVQAXwSOb91zD3BzwudKkjq5+2rqmLtyCwC9euTz1XPGZrkjSZIkSZIkSZIkSVIuSTToFGNcAPyo3XdfCSH8ABgJLIkxrkryTElS57d1VxP/9uS8tvVfnT6Kw/sVZ7EjSZIkSZIkSZIkSVKuSTToFEK4BlgVY3xi9+9jjGuANUmeJUnKHf8x/R3Wbm0AYFhJMX9x2sgsdyRJkiRJkiRJkiRJyjV5Cb/vNuC8hN8pScph9Ru286sXFretv37eWHr2yM9iR5IkSZIkSZIkSZKkXJR00GnlIXinJCmH/cvj82hoagHg+LISLplYmuWOJEmSJEmSJEmSJEm5KOlQ0uPAmSEEw06SJGa8u4GHZy5vW//DRRPIywtZ7EiSJEmSJEmSJEmSlKuSDiTdCPQFbg0hDEr43W1CCGUhhNtCCMtDCLtCCEtCCD8NIQz4AO/8aAihOYQQQwjf38u+D4cQHg0hrA8hbA8hzAoh/G0IYY/3MIUQLgohTA8hbAohbA0h/G8I4TMH26sk5YIYI99/ZHbb+oLjhnLiiIFZ7EiSJEmSJEmSJEmSlMsKEn7f74BNwDXAp0IIS0hdZxfb7YsxxrMP5oAQwijgJWAI8BAwFzgJ+DJwXgjh1BjjugN8Z1/gTmA70Gcv+y4B7gd2AvcC64FPAD8BTgWqOnjmb4BbgHXA3UADMAW4I4RwXIzx7w+kV0nKFY/MWsGMdzcC0CM/j2+cNz7LHUmSJEmSJEmSJEmSclnSQaczdvt9ETC29Vd77YNPB+KXpEJOX4ox3vLelyGEfwe+AtwM/NUBvvNnQAnww9bn04QQ+gG/ApqBM2KMNa3f/wPwLDAlhPCpGOM9uz0zAvgxqUBUZYxxSev33wVeA74aQrg/xvjyAfYrSZ3azsZm/vmxuW3r604dwRGH9cpiR5IkSZIkSZIkSZKkXJfo1XUxxrz9/LXHa972JoQwEjgHWAL8ol35H4FtwNUhhN4H8M5LgOuALwHL97J1CjAYuOe9kBNAjHEncFPr8vPtnvksqcDX//deyKn1mQ3AD1qXBxrKkqRO77YXF7Ns4w4ABvbuwRfOHJ3ljiRJkiRJkiRJkiRJuS7piU6H2lmtn0/GGFt2L8QYt4QQXiQVhDoZeGZfLwshDCE1penBGOPdIYRr9+PsxzuoPU/q2rsPhxCKYoy79uOZx9rt2VevtXsojduf5yUpU9Zs2cUvn1vYtv7Kx8ZQ0rMwix1JkiRJkiRJkiRJkrqCRCc6ZcB71+DN30N9Qevn0fv5vv8m9f/B/kxV2uPZMcYmYDGp4NjI/XxmBakJVGUhBO9zktRl/OTp+Wzd1QTA6CF9+PRJR2S5I0mSJEmSJEmSJElSV5DoRKcQwkf3d2+M8fmDOKKk9XPTHurvfd9/Xy8KIXwWuAS4Isa46hCdvT/P9G7dt31vh8cYKzr6vnXS0+S9PStJmTJ35WbuefXdtvWNF46nID/XMrWSJEmSJEmSJEmSpM4o6avrpgNxP/fmJ3w2QGj93GsPIYQRwE+B6hjjfZk8O4FnJKlTijFy87Q5tLT+jXbamEGccfTg7DYlSZIkSZIkSZIkSeoykg46fZeOQzv9gROBDwN/AGYc5Pvfm4xUsod6v3b79uQ2YAfwhUN89iZgUOsz6/byzOYD6EOSOqXp89fwwoK1AOQFuOnCCYQQ9vGUJEmSJEmSJEmSJEn7J9GgU4zxn/ZWDyFcC9wC3HiQR8xr/Tx6D/UxrZ/z9/GeyaTCR2v28EP4G0MINwIPxRgv3e3sytaza3ffHEIoAI4CmoBF7fod1PrMy+2eGUbq2rr6GONer62TpM6usbmFm6fNaVt/6qQjGDu0bxY7kiRJkiRJkiRJkiR1NUlPdNqrGOMdIYQ/B34AXHwQr3iu9fOcEEJejLHlvUIIoS9wKqlJTa/s4z13Ab06+H4M8FHgDVJhptd3qz0LXAmcB/yu3XMfbX3f8zHGXe2eObX1mZfbPXP+bnskKafd8+q7vLN6KwB9igr4ysf2lEeVJEmSJEmSJEmSJOng5GXhzJmkgkEHLMa4EHgSGAH8dbvyd0hNSLorxrjtvS9DCONCCOPavedLMcYb2v8Cbm/dMq31u1/s9thUYC3wqRBC5W7vLwa+37r8j3Y93Q7sAv4mhDBit2cGAN9qXf7nfv2Pl6ROatOORn7y9IK29V+fOZrBfYuy2JEkSZIkSZIkSZIkqSvK6ESnVuUf8NwvAC8BPw8hnA3MAT4EnEnqyrr21+K9d5dSh3fU7a8Y4+YQwl+QCjxNDyHcA6wnNZlqbOv397Z7ZnEI4WvAz4GaEMK9QAMwBSgD/i3G2H7SkyTllF889w7rtzUAUNq/J9edOiK7DUmSJEmSJEmSJEmSuqSMTXQKIeSHEG4gFfKpOdj3tE51qgTuIBVw+iowilSY6JQY47oP3u0ez34QOB14Hrgc+CLQCPwd8KkYY+zgmVtIhaHeBq4B/hJYCVwbY/z7Q9WrJGXC0nXbuOPFJW3rb5w/juLC/Ow1JEmSJBBTaqIAACAASURBVEmSJEmSJEnqshKd6BRCWLSXcw5v/Wzg/65tOygxxjrguv3cu9+TnGKMd5AKUO1tz4vABfv7ztZn/gD84UCekaRc8KPH59LQ3ALA5CP6c9Hxw7LckSRJkiRJkiRJkiSpq0r66ro8IG2qEampR28CrwK3xBjndLBHkpRDXluynkffXNm2vumiCYTwgW4JlSRJkiRJkiRJkiRpjxINOsUYRyT5PklS59TSEvneI7Pb1hdPHM7kIwZksSNJkiRJkiRJkiRJUleXl+0GJEm556GZy5hVvwmAooI8vn7e2Cx3JEmSJEmSJEmSJEnq6hINOoUQeoYQjggh9NhDvai1XpzkuZKkzNnR0My/PD6vbX3DaUdRNqBXFjuSJEmSJEmSJEmSJHUHSU90+jYwD+izh3pvYC7wrYTPlSRlyK9eWMSKTTsBGNSnB58/Y3SWO5IkSZIkSZIkSZIkdQdJB53OB56OMa7vqNj6/dPARQmfK0nKgFWbd/If0xe2rb96zlj6FBVksSNJkiRJkiRJkiRJUneRdNBpBDB/H3vmt+6TJOWYf3tyHjsamwEYN7Qvn6wsz3JHkiRJkiRJkiRJkqTuIumgUyHQso89EShO+FxJ0iH21rJNVNfWt61vunAC+Xkhix1JkiRJkiRJkiRJkrqTpINOi4DT97HnDGBpwudKkg6hGCM3T5tDjKn1WeOG8JExg7LblCRJkiRJkiRJkiSpW0k66PQwUBFC+HpHxRDCN4DJwIMJnytJOoSenrOalxetAyA/L/CtC8ZnuSNJkiRJkiRJkiRJUndTkPD7fgxcCfwwhPBJ4ElgGVAKnAtMAt4F/iXhcyVJh0hDUws/eHRO2/qqDx3B6CF9stiRJEmSJEmSJEmSJKk7SjToFGPcEEI4A/gf4BRS05siEFq3vARcFWPckOS5kqRD5+5XlrJ47TYA+hYX8OWPHZ3ljiRJkiRJkiRJkiRJ3VHSE52IMS4BTg0hTAZOBvoDG4FXYowzkj5PknTobNzewM+eWdC2/vLZYxjYu0cWO5IkSZIkSZIkSZIkdVeJB53e0xpqMtgkSTnsZ88sYNOORgCOPKwXV59yZJY7kiRJkiRJkiRJkiR1V3lJviyE0DOEcEQIocNxHyGEotZ6cZLnSpKSt2jNVn7z8tK29TfPH0dRQX4WO5IkSZIkSZIkSZIkdWeJBp2AbwPzgD57qPcG5gLfSvhcSVLCfvjYXJpaIgAnHTWQc48ZmuWOJEmSJEmSJEmSJEndWdJBp/OBp2OM6zsqtn7/NHBRwudKkhL00sK1PDV7Vdv6Hy6cQAghix1JkiRJkiRJkiRJkrq7pINOI4D5+9gzv3WfJKkTam6JfP+ROW3ryyaXclxZSRY7kiRJkiRJkiRJkiQp+aBTIdCyjz0RKE74XElSQu6fUc/sFZsBKC7M42vnjs1yR5IkSZIkSZIkSZIkJR90WgScvo89ZwBLEz5XkpSAbbua+PET89rWn/voKIaV9MxiR5IkSZIkSZIkSZIkpSQddHoYqAghfL2jYgjhG8Bk4MGEz5UkJeC//riQ1Vt2ATCkbxGfO31kljuSJEmSJEmSJEmSJCmlIOH3/Ri4EvhhCOGTwJPAMqAUOBeYBLwL/EvC50qSPqDlG3fw3y8salt/7dyx9OqR9D8TkiRJkiRJkiRJkiQdnER/gh1j3BBCOAP4H+AUUtObIhBat7wEXBVj3JDkuZKkD+7HT8xjZ2MLAMeW9uPyyWVZ7kiSJEmSJEmSJEmSpP+T+KiOGOMS4NQQwmTgZKA/sBF4JcY4I+nzJEkf3My6jTzw+rK29Y0XTCAvL+zlCUmSJEmSJEmSJEmSMuuQ3UnUGmoy2CRJnVyMke9Pm922PmfC4Zwy6rAsdiRJkiRJkiRJkiRJUrpDEnQKIQwDzgZKgaIOtsQY4/cOxdmSpAPz+FsreW1J6kbRwvzANy8Yn+WOJEmSJEmSJEmSJElKl3jQKYTwHeAb7d4dgNju9wadJCnLdjU188PH5ratrzllBEcN6p3FjiRJkiRJkiRJkiRJ6lheki8LIVwJ/APwAjCFVKjpTuDPgV8BLcA9wFlJnitJOjh3vrSEd9dvB6B/r0K+dNaYLHckSZIkSZIkSZIkSVLHkp7o9HmgHjgvxtgUQgBYEmO8B7gnhPB7YBrwu4TPlSQdoHVbd3HLM++0rf/27DGU9CrMYkeSJEmSJEmSJEmSJO1ZohOdgOOAR2OMTbt9l//eb2KMTwBPAF9L+FxJ0gH66dML2LIr9df1yEG9ufLkI7PckSRJkiRJkiRJkiRJe5Z00KkQWLfbegdQ0m7PW8DEhM+VJB2ABau28NtX321bf+uC8RTmJ/1PgiRJkiRJkiRJkiRJyUn6p9orgGG7rd8Fjm+3pxRoQpKUNT94dA7NLRGAU0cfxtnjh2S5I0mSJEmSJEmSJEmS9i7poNPrpK6ve8+zwGkhhKtDCL1DCBcCl7fukyRlwfPz1/DcvDUAhAA3XjCBEEKWu5IkSZIkSZIkSZIkae+SDjo9AhwTQjiqdf3PwCbgDmAz8DAQgJsSPleStB+amlv4/rTZbetPVpQzYXi/LHYkSZIkSZIkSZIkSdL+KUjyZTHGO0iFmt5b14UQTgS+CowClgC/jDG+meS5kqT9c19NPfNXbQWgV498vnru0VnuSJIkSZIkSZIkSZKk/ZNo0KkjMcbFwN8c6nMkSXu3ZWcj//7UvLb1F84YxZC+xVnsSJIkSZIkSZIkSZKk/Zf01XWSpE7ql9MXsnZrAwDDS4q54bSRWe5IkiRJkiRJkiRJkqT9Z9BJkrqBuvXbufVPi9vW/+/8cRQX5mexI0mSJEmSJEmSJEmSDoxBJ0nqBn70+FwamloAmFjen08cPzzLHUmSJEn6/9m782i/yvpc4M97MpKQhHkMEAIZBRVExKKIgAgJ9vaqvbZW2qooVq0WlSoFb6tClVJRsWq1rbVa26u32vZKGEVAZXDAATQnA4GEMIUQIPN83vvHOfyMMSecHJLsM3w+a2Vtv3vvd+8Hlvz+ybPeDQAAAADsGEUngAHurkVP5uq7H2nNH5w5LW1tpcFEAAAAAAAAALDjFJ0ABrBaaz5y9ezWPPPYg3PChH0aTAQAAAAAAAAAvaPoBDCAfevuR/KzxU8lSYYPacsHzp7acCIAAAAAAAAA6B1FJ4ABat3Gzbn82jmt+Y0vmZDD9hnVYCIAAAAAAAAA6D1FJ4AB6p++f38eemptkmTf0cPzjpcf3XAiAAAAAAAAAOg9RSeAAWjpyvX57M33tuYLXjE5Y0cOazARAAAAAAAAADw7ik4AA9CVN87N6g2bkySTDtgzv/fCwxpOBAAAAAAAAADPjqITwADT/siKfO1Hi1vzxTOnZegQP/cAAAAAAAAA9G/+5htgAKm15rJZ7emonfPLJu+fU6cc0GwoAAAAAAAAANgJFJ0ABpCb5z6W79/7eJKkrXTu5gQAAAAAAAAAA4GiE8AAsXFzRy6b1d6af//EwzP5wDENJgIAAAAAAACAnUfRCWCA+PcfPpAFS1cnScaMGJoLXjG54UQAAAAAAAAAsPMoOgEMAMvXbMwnbpzXmt9x2tHZb88RDSYCAAAAAAAAgJ1L0QlgAPi7m+fnyTUbkyTj994jf/xbE5oNBAAAAAAAAAA7maITQD+3aNnqfOn2ha35orOnZeSwIc0FAgAAAAAAAIBdQNEJoJ/76DVzsnFzTZK84Ii9M+PYgxpOBAAAAAAAAAA7n6ITQD/2g/uW5bpfPtqaP3jO9JRSGkwEAAAAAAAAALuGohNAP9XRUXPprPbW/DvPPyTPP2yvBhMBAAAAAAAAwK6j6ATQT/3nTx/KPQ8tT5KMGNqWC8+a2nAiAAAAAAAAANh1FJ0A+qE1Gzbliuvntua3vHRiDt1rjwYTAQAAAAAAAMCupegE0A/9w3fvz6Mr1iVJ9h8zIm879aiGEwEAAAAAAADArqXoBNDPLFmxLn9/64LW/L4zJ2fPEUMbTAQAAAAAAAAAu56iE0A/c8X1c7N24+YkydSDxuS1Lzis4UQAAAAAAAAAsOspOgH0I794aHm+8ZMHW/MHz5meIW2lwUQAAAAAAAAAsHsoOgH0E7XWXDprdmrtnM+YdkBOPnq/ZkMBAAAAAAAAwG6i6ATQT9wwe0nuvO+JJMnQtpKLZkxrOBEAAAAAAAAA7D6KTgD9wIZNHfnoNe2t+Q0nHZGj9t+zwUQAAAAAAAAAsHspOgH0A1+5c1EWLluTJBk7cmjeffqkhhMBAAAAAAAAwO6l6ATQxz25ekM+9e15rfldp0/K3qOHN5gIAAAAAAAAAHY/RSeAPu5TN83PinWbkiQT9h2VP3zxhGYDAQAAAAAAAEADFJ0A+rAFS1flX+9c1JovmjEtw4f66QYAAAAAAABg8PG35QB92Eevac+mjpokedGR++TM6Qc2nAgAAAAAAAAAmqHoBNBH3Xbv4/l2+2NJklKSD54zPaWUhlMBAAAAAAAAQDMUnQD6oM0dNZfOam/Nrzl+fI45dFyDiQAAAAAAAACgWYpOAH3Qf9y1OO2PrEiS7DFsSN535pSGEwEAAAAAAABAs/pl0amUMr6U8sVSysOllPWllIWllE+WUvbegWdcWEq5pmvtqlLKilLKPaWUK0sp47dx/1+VUuoz/Fmw1ZpTn+H+j+2Mfx/AwLJq/ab87Q3zWvP5L5uYg8aNbDARAAAAAAAAADRvaNMBdlQp5agktyc5IMl/J5mT5MQk705yVinl5Frrsh486vwkq5LcmmRJkmFJjktyQZI3l1JOrbX+dIv7b9nOs16V5Pgk13Zz/dZu1n+/BzmBQebzty7I0pXrkyQHjR2Zt54yseFEAAAAAAAAANC8fld0SvLZdJac3lVr/fTTJ0spV6azpHRZkrf14DnH1FrXbX2ylPKWJF/oes6Mp8/XWm/JNspKpZQhSd7cNX6hm3fdUmv9qx5kAga5h55amy98977WfOErp2TU8P74Uw0AAAAAAAAAO1e/+nRdKWVikjOTLEzyma0u/2WS1UnOLaWMfqZnbavk1OXrXcdJPYw1I8n4JHfWWu/u4RqAbbriujlZv6kjSXLMoWPzP487tOFEAAAAAAAAANA39LdtQk7rOt5Qa+3Y8kKtdWUp5bZ0FqFOSnJTL9/xqq5jT0tLb+06drebU5IcXUp5Z5KxSR5N8r1a6/wdCVVKuaubS1N35DlA3/WzxU/lv372cGv+4MzpaWsrDSYCAAAAAAAAgL6jvxWdpnQd53VzfX46i06T08OiUynlvHTuyLRnkmOTnJFkUZIP9GDtoUnOTrI8yde2c+sfdP3Zcu03kryl1vpkT3ICA1utNZdePbs1n/Wcg/Kiifs2mAgAAAAAAAAA+pb+VnQa13Vc3s31p8/vtQPPPC/Ji7aYf5Tk9bXWe3u4dkiSf621rtnG9aXpLEzNSufn9kYmOSHJXyd5TZKDSimnbL071bbUWl+wrfNdOz0d34OsQB92zT2P5seLOnuPw4aUfOBsm7UBAAAAAAAAwJbamg6wkz39jafa0wW11pNqrSXJfuncDSpJ7iqlnLXdF5XSluRNXeM2P1tXa/1lrfXyWusvaq2raq2P11qvS3JqkvuTnJxffSoPGKTWbdycj13X3pr/6MUTMmG/0Q0mAgAAAAAAAIC+p78VnZ7esWlcN9fHbnVfj9Val9Vab0xn2Wltki+XUvbYzpKzkxye5M5a6907+K4VSf6tazxlR7MCA8u/3L4wi59YmyTZe9Sw/OnpkxpOBAAAAAAAAAB9T38rOs3tOk7u5vrT7YB5vX1BrfWpJHck2T/Jc7Zz61u7jp/v5auWdh1t2wKD2OOr1ufvvvOrL2X+2RmTM26PYQ0mAgAAAAAAAIC+qb8VnW7uOp7Z9em4llLKmHR+Cm5tkjuf5XsO7Tpu2tbFUsohSWamc+eor/fyHSd1He/r5XpgAPjkt+dl5frOn5qj9h+d17/o8IYTAQAAAAAAAEDf1K+KTrXWBUluSDIhyTu2uvyhdO6O9OVa6+qnT5ZSppZSpm55YynliFLKxG29o5RyfpIXJlmc5J5uorw5yZAkX6m1rukubynl5K0LWV3n35DkdUk2pPdFKaCfm7dkZf7tBw+05otnTsuwIf3qZxkAAAAAAAAAdpuhTQfohbcnuT3JVaWU05O0J3lRkpen85N1F291f3vXsWxx7rgk3yyl3N61ZkmSfdO5y9KxSVYlObfWunnrl3cVl97cNX7hGbJ+NUlb13seTDIynSWqE9O5W9T5tdaFz/AMYIC6bFZ7Omrn/37J0fvl5VMOaDYQAAAAAAAAAPRh/a7oVGtdUEo5IcmHk5yVZEaSR5JcleRDtdYnevCYnyT5RJKXpvMTdPskWZfOz8h9PMmnaq2Lu1n7yiRHJLmz1trdjk9P+1ySM9L5Sb390lm2eijJl5J8stb68x5kBQagW+Y+llvnLU2StJXO3ZxKKc+wCgAAAAAAAAAGr35XdEqSrhLSG3t47280B2qtDyR5by/ffW1+fXeo7d17eZLLe/MeYODatLkjf31Ne2t+3QsPy7SDxzaYCAAAAAAAAAD6vramAwAMNv/nR4szb8mqJMno4UNywSsmN5wIAAAAAAAAAPo+RSeA3WjFuo35xI3zWvPbX350DhgzssFEAAAAAAAAANA/KDoB7EafvXlBlq3ekCQ5dK898uaXHNlwIgAAAAAAAADoHxSdAHaTxU+syRe/f39r/vOzpmTksCENJgIAAAAAAACA/kPRCWA3+dh1c7Jhc0eS5PmH7ZXfft4hDScCAAAAAAAAgP5D0QlgN7hr0ROZdfcjrfmD50xPKaXBRAAAAAAAAADQvyg6AexiHR01H766vTWf89yD84Ij9m4wEQAAAAAAAAD0P4pOALvYt+5+OD9f/FSSZPjQtrz/rKkNJwIAAAAAAACA/kfRCWAXWrdxcy6/dk5rfvNLjsxh+4xqMBEAAAAAAAAA9E+KTgC70D9+7748vHxdkmTf0cPz9lOPajgRAAAAAAAAAPRPik4Au8hjK9fls7csaM3vOXNyxowc1mAiAAAAAAAAAOi/FJ0AdpErb5iXNRs2J0kmH7hnXnfCYQ0nAgAAAAAAAID+S9EJYBeY/fCKfO3Hi1vzJTOnZ+gQP7kAAAAAAAAA0Fv+1h1gJ6u15rJrZqfWzvnUKfvnlMn7NxsKAAAAAAAAAPo5RSeAnew7cx7LbfcuS5IMaSu5eMa0hhMBAAAAAAAAQP+n6ASwE23c3JHLrmlvza8/8fBMOnBMg4kAAAAAAAAAYGBQdALYib5656Lct3R1kmTMiKH5szMmNZwIAAAAAAAAAAYGRSeAnWT5mo355E3zW/M7Tzs6++45osFEAAAAAAAAADBwKDoB7CSf/s78PLVmY5LksH32yB+fPKHZQAAAAAAAAAAwgCg6AewE9z++Ov9yx8LWfNHZ0zJi6JDG8gAAAAAAAADAQKPoBLATfOza9mzcXJMkL5ywd84+5qCGEwEAAAAAAADAwKLoBPAs3Xnfslz/yyWt+ZKZ01NKaTARAAAAAAAAAAw8ik4Az0JHR82ls2a35v953KF53mF7NZgIAAAAAAAAAAYmRSeAZ+GbP30ov3hoRZJk5LC2XPjKKQ0nAgAAAAAAAICBSdEJoJfWbNiUK66f05rf+tKJOWSvPRpMBAAAAAAAAAADl6ITQC99/tb7smTF+iTJ/mNG5PyXHdVwIgAAAAAAAAAYuBSdAHrh0eXr8vnvLmjNF545JaNHDG0wEQAAAAAAAAAMbIpOAL1wxfVzs25jR5Jk+sFj85oXjG84EQAAAAAAAAAMbIpOADvongeX5xs/ebA1XzJzWoa0lQYTAQAAAAAAAMDAp+gEsANqrfnIrNmt+YxpB+a3jt6vwUQAAAAAAAAAMDgoOgHsgOt/uSQ/vP+JJMnQtpK/mDG14UQAAAAAAAAAMDgoOgH00PpNm/PRa9tb87kvPiIT99+zwUQAAAAAAAAAMHgoOgH00FfuWJRFy9YkScbtMSzvPn1Sw4kAAAAAAAAAYPBQdALogSdWb8inbprfmt91+qTsNWp4g4kAAAAAAAAAYHBRdALogatump+V6zYlSY7cb3TOPemIhhMBAAAAAAAAwOCi6ATwDO59bFW+cuei1nzR2VMzfKifTwAAAAAAAADYnfxNPcAz+Og17dncUZMkJ03cJ6+YfmDDiQAAAAAAAABg8FF0AtiO789/PDfNeSxJUkpyyczpKaU0nAoAAAAAAAAABh9FJ4BubO6ouXTW7Nb82uPH55hDxzWYCAAAAAAAAAAGL0UngG783x8vzpxHVyZJRg0fkve9ckrDiQAAAAAAAABg8FJ0AtiGVes35W9vmNea3/ayo3Lg2JENJgIAAAAAAACAwU3RCWAbPnfLvXl81fokyUFjR+YtL53YcCIAAAAAAAAAGNwUnQC28uCTa/IP37u/Nf/5WVOyx/AhDSYCAAAAAAAAABSdALZyxfVzs2FTR5LkuePH5Xeef2jDiQAAAAAAAAAARSeALfz0gSfz3z97uDVfMnN62tpKg4kAAAAAAAAAgETRCaCl1pqPXD27NZ99zEE58ch9GkwEAAAAAAAAADxN0Qmgy6x7HslPHngqSTJ8SFs+cPbUhhMBAAAAAAAAAE9TdAJIsm7j5nzs2jmt+Y9PnpAj9h3dYCIAAAAAAAAAYEuKTgBJ/vm2hXnwybVJkr1HDcs7Xn50w4kAAAAAAAAAgC0pOgGD3uOr1uczN9/bmi94xeSM22NYg4kAAAAAAAAAgK0pOgGD3pU3zsuq9ZuSJEcfsGdef+LhDScCAAAAAAAAALam6AQManMfXZn/88MHWvPFM6Zl6BA/jQAAAAAAAADQ1/jbfGBQu+ya9nTUzv/90kn75dQp+zcbCAAAAAAAAADYJkUnYNC6Ze5j+e68pUmStpJcPHNaSikNpwIAAAAAAAAAtkXRCRiUNm3uyGWz2lvz6154eKYeNLbBRAAAAAAAAADA9ig6AYPSv/9oceY/tipJsueIoXnPKyY3nAgAAAAAAAAA2B5FJ2DQWbFuYz5x47zW/PaXH5X9x4xoMBEAAAAAAAAA8EwUnYBB5zPfuTdPrN6QJDl0rz3yppOPbDgRAAAAAAAAAPBMFJ2AQeWBZWvyz7ctbM3vP3tqRg4b0lwgAAAAAAAAAKBHFJ2AQeXy6+Zkw+aOJMlxh++VVz334IYTAQAAAAAAAAA9oegEDBo/WvhEZt3zSGv+4DnTU0ppMBEAAAAAAAAA0FOKTsCg0NFRc+nVs1vzq553SI4/fO8GEwEAAAAAAAAAO0LRCRgU/t/PH87PH1yeJBk+tC3vP2tKw4kAAAAAAAAAgB2h6AQMeGs3bM7l181pzee95MiM33tUg4kAAAAAAAAAgB2l6AQMeP/4vfvyyPJ1SZL99hyePzn1qIYTAQAAAAAAAAA7StEJGNAeW7Eun7t1QWt+75lTMmbksAYTAQAAAAAAAAC9oegEDGh/e8PcrNmwOUky9aAx+V8nHNZwIgAAAAAAAACgNxSdgAHrlw8vz/+968HWfPHMaRnSVhpMBAAAAAAAAAD0lqITMCDVWnPZrPbU2jmfNvWAvHTS/s2GAgAAAAAAAAB6TdEJGJC+3f5Ybl+wLEkypK3kL2ZMbTgRAAAAAAAAAPBsKDoBA86GTR3562vaW/MfvOjwHH3AmAYTAQAAAAAAAADPlqITMOB89QeLcv/jq5MkY0YOzZ+dMbnhRAAAAAAAAADAs6XoBAwoT63ZkE9+e35rftdpk7LP6OENJgIAAAAAAAAAdgZFJ2BAueqme7N87cYkyeH7jMof/tYRDScCAAAAAAAAAHaGfll0KqWML6V8sZTycCllfSllYSnlk6WUvXfgGReWUq7pWruqlLKilHJPKeXKUsr4btbU7fy5czvvOqeUckspZXnXu35QSvmj3vyzA927b+mqfPmOha35orOnZsTQIY3lAQAAAAAAAAB2nqFNB9hRpZSjktye5IAk/51kTpITk7w7yVmllJNrrct68Kjzk6xKcmuSJUmGJTkuyQVJ3lxKObXW+tNtrFuU5EvbOP9gN3nfmeTTSZYl+dckG5K8NsmXSinH1lrf14OsQA987No52dRRkyQnTtgnZx1zUMOJAAAAAAAAAICdpd8VnZJ8Np0lp3fVWj/99MlSypXpLCldluRtPXjOMbXWdVufLKW8JckXup4zYxvrFtZa/6onQUspE5L8bZInkpxQa13Ydf7DSX6U5L2llG/UWu/oyfOA7t2+4PHcMHtJa77knGkppTSYCAAAAAAAAADYmfrVp+tKKROTnJlkYZLPbHX5L5OsTnJuKWX0Mz1rWyWnLl/vOk7qZcwtvSnJiCR/93TJqevdTyb5666xJ6UsYDs2d9RcenV7a371cYfmueP3ajARAAAAAAAAALCz9bcdnU7rOt5Qa+3Y8kKtdWUp5bZ0FqFOSnJTL9/xqq7j3d1c36uU8qYkByVZnuSuWuudz5D3um1cu3are7arlHJXN5em9mQ9DGTf/MmDmf3IiiTJyGFtufCsKQ0nAgAAAAAAAAB2tv5WdHq6vTCvm+vz01l0mpweFp1KKeclGZ9kzyTHJjkjyaIkH+hmyfOS/NNWz/h5knNrrff0NG+t9ZFSyuok40spo2qta3qSF/h1q9dvyhXXz23Nbz3lqBw8bo8GEwEAAAAAAAAAu0J/KzqN6zou7+b60+d35JtV5yV50Rbzj5K8vtZ67zbuvTLJN9JZXFqXzt2U3p/ktUm+U0p5fq31oR3MO7rrvu0WnWqtL9jW+a6dno7f3loYyD7/3fvy2Mr1SZIDxozI+adMbDgRAAAAAAAAALArtDUdYCcrXcfa0wW11pNqrSXJfuncDSpJ7iqlnLWNe99ba7291vp4rXVVrfXHtdbfTWf5ab8k79vVeYFfeWT52nzhuwta84WvIAK96wAAIABJREFUnJLRI/pbfxMAAAAAAAAA6In+VnR6emekcd1cH7vVfT1Wa11Wa70xnWWntUm+XErp6fev/r7reMpW53uad0WPgwItV1w3N+s2diRJnnPI2Lzm+PENJwIAAAAAAAAAdpX+VnSa23Wc3M31SV3Heb19Qa31qSR3JNk/yXN6uGxp13H0Vue7zVtKObjr/gdrrdv9bB3wm+5+8Kl886e/+lLkJTOnp62tbGcFAAAAAAAAANCf9bei081dxzNLKb+WvZQyJsnJ6dyN6c5n+Z5Du46benj/SV3H+7Y6/52u4298Bi/J2VvdA/RQrTWXXt3ems+cfmBefNS+DSYCAAAAAAAAAHa1flV0qrUuSHJDkglJ3rHV5Q+lc4ekL9daVz99spQytZQydcsbSylHlFImbusdpZTzk7wwyeIk92xx/vhSytY7NqWU8twkl3WN/7rV5X9Osj7JO0spE7ZYs3eSv+ga/z7ADrnuF4/mhwufSJIMbSu5aMa0hhMBAAAAAAAAALva0KYD9MLbk9ye5KpSyulJ2pO8KMnL0/nJuou3uv/pbV+2/KbVcUm+WUq5vWvNkiT7pnNnpmOTrEpybq118xZr3pXk1aWU76SzBLU+ydR07tY0JMk/JPn3LV9ca72/lHJhkquS/LiU8rUkG5K8Nsn4JB+vtd7Ry38PMCit37Q5H712Tmv+wxdPyJH7/UYHEQAAAAAAAAAYYPpd0anWuqCUckKSD6ezZDQjySPpLBN9qNb6RA8e85Mkn0jy0iQzk+yTZF06Pz338SSfqrUu3mrNfyUZm+S5SU5LMjLJsiTXJvmHWuv/6ybvp0spC5O8L8kfpnMXrdlJLqm1/ksP/7GBLl++fVEeeGJNkmSvUcPy7tMnNZwIAAAAAAAAANgd+l3RKUm6Skhv7OG9ZRvnHkjy3h1853+ls+y0w2qt30ryrd6sBX5l2ar1ueo781vzu0+flHGjhjWYCAAAAAAAAADYXdqaDgDQU5+6aX5WrtuUJJm43+i84aQjGk4EAAAAAAAAAOwuik5Av3DvYyvz1R880JovmjEtw4b4CQMAAAAAAACAwUJLAOgXLpvVns0dNUnyW0ftmzOmHdBwIgAAAAAAAABgd1J0Avq8785bmpvnLk2SlJJcPHNaSikNpwIAAAAAAAAAdidFJ6BP29xRc9ms9tb8uy8Yn+ccMq7BRAAAAAAAAABAExSdgD7t6z9enLlLViZJRg0fkvedOaXhRAAAAAAAAABAExSdgD5r5bqN+fgNc1vzn7zsqBwwdmSDiQAAAAAAAACApig6AX3W525ZkMdXbUiSHDxuZM576cSGEwEAAAAAAAAATVF0AvqkB59ck3/8/v2t+f1nTc0ew4c0mAgAAAAAAAAAaJKiE9AnXX7d3GzY1JEked74cfnt5x3ScCIAAAAAAAAAoEmKTkCfc9eiJ/Otnz/cmi85Z3ra2kqDiQAAAAAAAACApik6AX1KrTWXzprdmmcee3BeOGGfBhMBAAAAAAAAAH2BohPQp3zr7kfy0weeSpIMH9KW9581teFEAAAAAAAAAEBfoOgE9BnrNm7O5dfOac1vPHlCDt93VIOJAAAAAAAAAIC+QtEJ6DO+eNv9eeiptUmSfUYPzztOO7rhRAAAAAAAAABAX6HoBPQJS1euz2dvXtCaL3jF5IwdOazBRAAAAAAAAABAX6LoBPQJV944L6vWb0qSTDpgz/z+Cw9rOBEAAAAAAAAA0JcoOgGNm/PoinztRw+05otnTsvQIX6eAAAAAAAAAIBf0SQAGlVrzWWz2tNRO+dTJu+fU6cc0GwoAAAAAAAAAKDPUXQCGnXL3KX53vzHkyRtJbl4xrSGEwEAAAAAAAAAfZGiE9CYjZs7cums2a359048PFMOGtNgIgAAAAAAAACgr1J0Ahrz7z98IAuWrk6S7DliaN7ziskNJwIAAAAAAAAA+ipFJ6ARy9duzCdunNea3/Hyo7PfniMaTAQAAAAAAAAA9GWKTkAjPnPzvXlyzcYkyfi998gbT57QbCAAAAAAAAAAoE9TdAJ2u0XLVuefb7u/NX/g7KkZOWxIg4kAAAAAAAAAgL5O0QnY7T527Zxs3FyTJMcfvldmHntww4kAAAAAAAAAgL5O0QnYrX54/xO59hePtuYPnjM9pZQGEwEAAAAAAAAA/YGiE7DbdHTUXDprdmv+H88/JMcdvneDiQAAAAAAAACA/kLRCdht/utnD+XuB5cnSUYMbcufnzW14UQAAAAAAAAAQH+h6ATsFms3bM7fXDe3NZ/30iNz6F57NJgIAAAAAAAAAOhPFJ2A3eIfvndfHl2xLkmy354j8ienHt1wIgAAAAAAAACgP1F0Ana5JSvW5XO3LGjN7ztzcvYcMbTBRAAAAAAAAABAf6PoBOxyf3v93KzduDlJMvWgMfndEw5rOBEAAAAAAAAA0N8oOgG71C8eWp7/+MmDrfmSmdMzpK00mAgAAAAAAAAA6I8UnYBdptaaS2fNTq2d8+lTD8hLJu3XbCgAAAAAAAAAoF9SdAJ2mRtnL8md9z2RJBnaVnLRjGkNJwIAAAAAAAAA+itFJ2CX2LCpIx+9dk5rfsNJR+ToA/ZsMBEAAAAAAAAA0J8pOgG7xFfuXJT7H1+dJBk7cmjeffqkhhMBAAAAAAAAAP2ZohOw0z21ZkOuuml+a37X6ZOy9+jhDSYCAAAAAAAAAPo7RSdgp/vUTfOzfO3GJMkR+47KuS8+ouFEAAAAAAAAAEB/p+gE7FQLlq7KV+5Y1JovOntaRgwd0mAiAAAAAAAAAGAgUHQCdqqPXjMnmzpqkuTEI/fJK59zYMOJAAAAAAAAAICBQNEJ2Gluv/fxfLt9SZKklOSDM6enlNJwKgAAAAAAAABgIFB0AnaKzR01H5nV3ppffdz4HDt+XIOJAAAAAAAAAICBRNEJ2Cm+cdeDaX9kRZJk5LC2XPjKKQ0nAgAAAAAAAAAGEkUn4FlbvX5Trrhhbms+/5SjctC4kQ0mAgAAAAAAAAAGGkUn4Fn7/K0LsnTl+iTJgWNH5PyXTWw4EQAAAAAAAAAw0Cg6Ac/Kw0+tzRe+d19rvvCVUzNq+NAGEwEAAAAAAAAAA5GiE/CsXHH93Kzb2JEkOebQsXn1cYc2nAgAAAAAAAAAGIgUnYBe+/nip/KfP32oNV8yc3ra2kqDiQAAAAAAAACAgUrRCeiVWms+cvXs1vzK5xyYkybu22AiAAAAAAAAAGAgU3QCeuXaXzyaHy96MkkybEjJRWdPazgRAAAAAAAAADCQKToBO2z9ps356LXtrfmPXjwhE/Yb3WAiAAAAAAAAAGCgU3QCdtiXbluYxU+sTZLsNWpY/vS0SQ0nAgAAAAAAAAAGOkUnYIcsW7U+f/ede1vzn50+KeNGDWswEQAAAAAAAAAwGCg6ATvkk9+en5XrNyVJJu4/On9w0hENJwIAAAAAAAAABgNFJ6DH5i9ZmX/74QOt+eIZ0zJsiJ8RAAAAAAAAAGDX01AAeuyya9qzuaMmSU4+et+cNvWAhhMBAAAAAAAAAIOFohPQI7fOW5pb5i5NkpSSXDJzekopDacCAAAAAAAAAAYLRSfgGW3a3JHLZs1uza874bBMO3hsg4kAAAAAAAAAgMFG0Ql4Rl/78eLMW7IqSTJq+JC858zJDScCAAAAAAAAAAYbRSdgu1au25grb5jXmt9+6lE5YMzIBhMBAAAAAAAAAIORohOwXZ+5eUGWrd6QJDlk3Mic99KJDScCAAAAAAAAAAYjRSegW4ufWJMvfv/+1vz+s6dm5LAhDSYCAAAAAAAAAAYrRSegW5dfNycbNnckSZ532F551XMPaTgRAAAAAAAAADBYKToB23TXoidy9d2PtOb/fc60tLWVBhMBAAAAAAAAAIOZohPwGzo6aj58dXtrnvncg/OCI/ZpMBEAAAAAAAAAMNgpOgG/4Vt3P5yfL34qSTJ8SFs+cNbUhhMBAAAAAAAAAIOdohPwa9Zt3Jy/uW5ua37TS47MYfuMajARAAAAAAAAAICiE7CVf/r+/XnoqbVJkn1HD8/bX35Uw4kAAAAAAAAAABSdgC08tnJdPnvzva35gldMztiRwxpMBAAAAAAAAADQSdEJaPnEjfOyesPmJMnkA/fM773wsIYTAQAAAAAAAAB0UnQCkiTtj6zI1360uDVfPHN6hg7xEwEAAAAAAAAA9A1aDEBqrbl01ux01M75ZZP3z8sm799sKAAAAAAAAACALfTLolMpZXwp5YullIdLKetLKQtLKZ8spey9A8+4sJRyTdfaVaWUFaWUe0opV5ZSxm/j/kNLKX9aSrm2a836UsqyUsqNpZRXd/OOU0spdTt/PvZs/j3AznLz3Mdy273LkiRD2koumTmt4UQAAAAAAAAAAL9uaNMBdlQp5agktyc5IMl/J5mT5MQk705yVinl5Frrsh486vwkq5LcmmRJkmFJjktyQZI3l1JOrbX+dIv7/zTJ+5Pcn+TmJI8mOSLJq5OcUUr5RK31Pd2869Ykt2zj/Pd7kBN2qY2bO3LprPbW/PsnHpZJB45pMBEAAAAAAAAAwG/qd0WnJJ9NZ8npXbXWTz99spRyZTpLSpcleVsPnnNMrXXd1idLKW9J8oWu58zY4tIPk5xaa711q/unJbkzyQWllK/WWu/axrtuqbX+VQ8ywW73bz94IPctXZ0kGTNiaC44Y3LDiQAAAAAAAAAAflO/+nRdKWVikjOTLEzyma0u/2WS1UnOLaWMfqZnbavk1OXrXcdJW93/za1LTl3n25N8rWs89ZneC33J8jUb88lvz2vN7zzt6Oy754gGEwEAAAAAAAAAbFu/KjolOa3reEOttWPLC7XWlUluSzIqyUnP4h2v6jrevQNrNnYdN3Vz/ehSyjtLKX9RSnlTKWVSN/fBbvXp78zPk2s6/+972D575I9+a0KzgQAAAAAAAAAAutHfPl03pes4r5vr89O549PkJDf15IGllPOSjE+yZ5Jjk5yRZFGSD/Rw/dgkr0lSk9zQzW1/0PVny3XfSPKWWuuTPXzPtj6JlyRTe7Ietrbw8dX5lzsWtuYPnDUtI4cNaSwPAAAAAAAAAMD29Lei07iu4/Jurj99fq8deOZ5SV60xfyjJK+vtd77TAtLKSXJPyY5MMlnuz5jt6Wl6SxMzUrn5/ZGJjkhyV+nsxx1UCnllK13p4Ld4WPXzsnGzTVJcsIRe2fGsQc1nAgAAAAAAAAAoHv9rej0TErXsfZ0Qa31pCQppeyb5PgklyW5q5Tyulrrdc+w/ONJfjfJ95K8ZxvP/mWSX25xalWS60optyf5WZKT0/mpvP/uQc4XbOt8105Pxz/TetjSnfcty3W/fLQ1X3LO9HT29gAAAAAAAAAA+qa2pgPsoKd3bBrXzfWxW93XY7XWZbXWG9P56bu1Sb5cStmju/tLKVckuSDJd5PMqLWu34F3rUjyb13jKTuaFZ6Njo6aS2fNbs2/8/xD8vzDdmQTNAAAAAAAAACA3a+/FZ3mdh0nd3N9UtdxXm9fUGt9KskdSfZP8pxt3VNK+USS9yW5OcnZtdZVvXjV0q7j6N7khN76z58+lF88tCJJMmJoWy48a2rDiQAAAAAAAAAAnll/Kzrd3HU8s5Tya9lLKWPS+Sm4tUnufJbvObTruGmrd5RSymeS/FmSG5PMrLWu6eU7Tuo63tfL9bDD1mzYlL+5fk5rfuspE3PoXt1uXAYAAAAAAAAA0Gf0q6JTrXVBkhuSTEjyjq0ufyiduyN9uda6+umTpZSppZRf27KmlHJEKWXitt5RSjk/yQuTLE5yzxbnS5IvJHl7kmuT/Hatde328pZSTt66kNV1/g1JXpdkQ5Kvb+8ZsDN94bv3ZcmKzq8s7j9mRN72sqMaTgQAAAAAAAAA0DNDmw7QC29PcnuSq0oppydpT/KiJC9P5yfrLt7q/vauY9ni3HFJvllKub1rzZIk+6Zzl6Vjk6xKcm6tdfMWa/53kvPSuWPUz5J8oLP79Gt+Vmv9ry3mryZp63rPg0lGprNEdWI6d4s6v9a6cEf+4aG3Hl2+Lp+/9VcbiL3vzMkZPaI//gQAAAAAAAAAAINRv2s51FoXlFJOSPLhJGclmZHkkSRXJflQrfWJHjzmJ0k+keSlSWYm2SfJunR+Ru7jST5Va1281Zoju457JLmom+f+S5Iti06fS3JGOj+pt186y1YPJflSkk/WWn/eg6ywU1xx/dys3djZ3Zt28Ni89gWHNZwIAAAAAAAAAKDn+l3RKUm6Skhv7OG9v7HtUq31gSTv3cF3/nGSP97BNZcnuXxH1sCucM+Dy/ONnzzYmi+ZOS1D2n7jPw0AAAAAAAAAgD6rrekAwK5Va82ls2a35jOmHZCTj96vwUQAAAAAAAAAADtO0QkGuBtmL8kP7u/8ouPQtpKLZkxrOBEAAAAAAAAAwI5TdIIBbMOmjnz0mvbW/IaTjshR++/ZYCIAAAAAAAAAgN5RdIIB7Mt3LMzCZWuSJOP2GJZ3nz6p2UAAAAAAAAAAAL2k6AQD1JOrN+Sqm+a35nedPil7jx7eYCIAAAAAAAAAgN5TdIIB6lM3zc+KdZuSJBP2HZVzTzqi4UQAAAAAAAAAAL2n6AQD0L2PrcpX7lzUmi+aMS3Dh/rPHQAAAAAAAADovzQfYAD62LXt2dxRkyQnTdwnZ04/sOFEAAAAAAAAAADPjqITDDC33ft4vt3+WJKklOSSmdNTSmk4FQAAAAAAAADAs6PoBAPI5o6aj1w9uzW/5vjxOebQcQ0mAgAAAAAAAADYORSdYAD5j7sWZ86jK5MkewwbkgtfOaXhRAAAAAAAAAAAO4eiEwwQq9ZvyhXXz2vNb3vZUTlw7MgGEwEAAAAAAAAA7DyKTjBA/P0tC/L4qvVJkoPGjsxbTjmy4UQAAAAAAP+/vTuPtqus0wT8/pIQZgIyqaAICCFO3YgyKTLJICi2U5Vli9WWoqjlPFAqaluiQrmcKIey7VLaoVsRLbUFAZVJxaEEZxLCFImMkjBDAkm++uOce+t6TYCQ3LPPvXmete768u397b3fk7Wyzr3nvtkbAABg7VF0gingmlvuzmd/eOXo/K2Hzc5GM2d0mAgAAAAAAAAAYO1SdIIp4J/OnJely1YkSR6/3aw8Z/ftOk4EAAAAAAAAALB2KTrBJPfLq2/Ot3517ej8+CPnZNq06jARAAAAAAAAAMDap+gEk1hrLSecPnd0fvhjH5q9dtqyw0QAAAAAAAAAABND0QkmsdN/e10u+sPNSZL1plfefsRuHScCAAAAAAAAAJgYik4wSS25d3lO/O680fn/2PdR2WHLjTtMBAAAAAAAAAAwcRSdYJI65cIF+ePNdydJtthovfz9Qbt0nAgAAAAAAAAAYOIoOsEkdNMdS/PJcy4fnb/xkF0za8P1OkwEAAAAAAAAADCxFJ1gEvro9+bn9qXLkiQ7b71x/mbPR3acCAAAAAAAAABgYik6wSQz/4bb8/9+fvXo/J1Hzsl60/1TBgAAAAAAAACmNu0ImGTef/rcrGi9P++3y1Y5cPY23QYCAAAAAAAAABgARSeYRM679MacP/9PSZJp1bubU1V1nAoAAAAAAAAAYOIpOsEksWz5irz/9Lmj879+8iOy20M36zARAAAAAAAAAMDgKDrBJPGVf1+Yy268I0my8czpedMhsztOBAAAAAAAAAAwOIpOMAnctuTefPR780fnrz7w0dl60/U7TAQAAAAAAAAAMFiKTjAJfPLcy7PoznuSJNttvmFe9tQdO04EAAAAAAAAADBYik4w5BYuviuf/9GC0flxz9gtG6w3vbtAAAAAAAAAAAAdUHSCIXfid+flnuUrkiS7P3LzPOsJD+s4EQAAAAAAAADA4Ck6wRD7xYLFOf23143Ojz/yMamqDhMBAAAAAAAAAHRD0QmG1IoVLe87fe7o/JlPeFj22GGLDhMBAAAAAAAAAHRH0QmG1Ld/fW1+vfCWJMnMGdNy3OG7dZwIAAAAAAAAAKA7ik4whO6+Z3lOOnPe6PxlT90xj3jIRh0mAgAAAAAAAADolqITDKF//dGVue7WJUmSrTaZmVcfsHPHiQAAAAAAAAAAuqXoBEPmxtuW5FPnXTE6f9Mhs7PpBut1mAgAAAAAAAAAoHuKTjBkPnz2/Nx1z/IkyextN81fPWn7jhMBAAAAAAAAAHRP0QmGyCXX3pZTL1o4On/nkXMyY7p/pgAAAAAAAAAAGhQwJFprOeH0S9Jab37g7K3ztF237jYUAAAAAAAAAMCQUHSCIfGDuTfmwisWJUmmT6u844g5HScCAAAAAAAAABgeik4wBO5dviIfOGPu6PxFez4yu2y7aYeJAAAAAAAAAACGi6ITDIEv//QPufKmO5Mkm24wI294+i4dJwIAAAAAAAAAGC6KTtCxW++6Nx/7wWWj89ce9Ohsucn6HSYCAAAAAAAAABg+ik7QsZPPuSy33HVvkuSRD9kof7vvo7oNBAAAAAAAAAAwhBSdoENX3XRnvvCTBaPztz9jt6w/Y3pneQAAAAAAAAAAhpWiE3Tog2fMzb3LW5LkyY/aIoc/7qEdJwIAAAAAAAAAGE6KTtCRn1yxKGdfcsPo/PgjH5Oq6jARAAAAAAAAAMDwUnSCDqxY0XLC6ZeMzp+7+3b5L4/YvMNEAAAAAAAAAADDTdEJOvD1i/+Y3197W5Jkg/Wm5S2Hze44EQAAAAAAAADAcFN0ggG7655l+dBZl47OX7HfTnn45ht2mAgAAAAAAAAAYPgpOsGAfeb8K3Pj7UuTJNtsun5euf/OHScCAAAAAAAAABh+ik4wQNfdenc+c8EVo/O3HDY7G68/o8NEAAAAAAAAAACTg6ITDNCHzro0S+5dkSR5zMM2y/OeuH3HiQAAAAAAAAAAJgdFJxiQ3/zxlnzj4mtG58c/c06mT6sOEwEAAAAAAAAATB6KTjAArbWc8J25o/NDHrNt9t15qw4TAQAAAAAAAABMLopOMABn/f76/HzB4iTJjGmVtz9jt44TAQAAAAAAAABMLopOMMGWLlueD3533uj86H12yE5bb9JhIgAAAAAAAACAyUfRCSbYFy78Q/6w6K4kyawN18vrD96l40QAAAAAAAAAAJOPohNMoMV33pOTz7lsdP76g3fJ5hvN7DARAAAAAAAAAMDkpOgEE+jj35+f25csS5LsuNXGefHeO3ScCAAAAAAAAABgclJ0ggly+Y135Es/u3p0/o4j5mTmDP/kAAAAAAAAAAAeDK0LmCAfOGNulq9oSZJ9dtoyT5+zTceJAAAAAAAAAAAmL0UnmAA/vOxPOWfejUmSquT4Z85JVXWcCgAAAAAAAABg8lJ0grVs+YqW958+d3T+gj22z2MfPqvDRAAAAAAAAAAAk5+iE6xlp/5iYeZdf3uSZKOZ0/PmQ2d3nAgAAAAAAAAAYPJTdIK16I6ly/Lhsy8dnR+7/87ZdrMNOkwEAAAAAAAAADA1KDrBWvTD+X/KojvvSZI8bNYGOWa/nTpOBAAAAAAAAAAwNSg6wVr0jMc/LN957VOzz05b5m2Hz86GM6d3HQkAAAAAAAAAYEqY0XUAmGoe+/BZ+b/H7NV1DAAAAAAAAACAKUXRCSZAVXUdAQAAAAAAAABgSvHoOgAAAAAAAAAAYOgpOgEAAAAAAAAAAENP0QkAAAAAAAAAABh6k7LoVFXbV9XnquraqlpaVQuq6mNVtcVqnOOtVXVG/9g7quq2qvptVX2kqra/j+MeU1WnVtWNVbWkqi6tqvdW1Yb3ccy+/Wstrqq7quo3VfWGqpq+uq8dAAAAAAAAAADWRTO6DrC6qmrnJBcm2SbJt5LMS7JnktcnObyqntJaW/QATvXKJHckOT/JDUnWS7J7kjcmeVlVHdBa++W4a++V5Jz+2tOSLExyUJJ3Jzm4qg5urS0dd8yzk3w9yZIkX02yOMmzknw0yVOSvGB1/w4AAAAAAAAAAGBdM+mKTkk+lV7J6XWttX8e2VhVH0mvpPT+JMc+gPM8rrW2ZPzGqjomyf/qn+eIMdunJ/l8ko2SPLu19u3+9mlJTk3yvP71TxxzzGZJPptkeZIDWmu/6G9/V3qFqedX1Qtba195wK8eAAAAAAAAAADWQZPq0XVVtVOSQ5MsSPLJcbvfk+TOJEdX1cb3d66VlZz6Tu2Pu4zbvn+SOUkuGCk59c+zIsnb+tNjq6rGHPP8JFsn+cpIyWnMtY/vT191f1kBAAAAAAAAAGBdN6mKTuk9Ji5Jzu4XjEa11m5P8uP07ri09xpc41n98TeruPaZ4w9orV2ZZH6SHZLs9ECOSXJBkruS7FtV6z/otAAAAAAAAAAAsA6YbI+um90f569i/2Xp3fFp1yQ/eCAnrKqXJ9k+ySZJHp/k6Un+kOQfHsS1d+1/XXF/x7TWllXVVUkem145au795LxoFbt2u6/jAAAAAAAAAABgKphsRadZ/fHWVewf2b75apzz5Un2GjP/9yQvaq1dvhauPRF5AQAAAAAAAABgnTPZik73p/pje6AHtNb2TpKq2jLJE5O8P8lFVfXXrbWVPXJurV17dY5pre2x0hP07vT0xNW4JgAAAAAAAAAATDrTug6wmkbugDRrFfs3G7fuAWutLWqtfS+9R9/dneQLVbXhGl57wvICAAAAAAAAAMC6ZLIVnS7tj7uuYv8u/XH+g71Aa+2WJD9JsnWSx67htVd5TFXNSLJjkmVJrnyweQEAAAAAAAAAYF0w2YpO5/bHQ6vqz7JX1aZJnpLe3Zh+uobX2a4/Lhuz7Zz+ePj4xVW1U3plpj/kz0tLqzwmydOSbJTkwtba0jVKCwAAAAAAAAAAU9ykKjq11q5IcnaSRyV5zbjd702ycZIvtNbuHNlYVbtV1W5jF1bVDv1y0l+oqlcmeXKShUl+O2bX+UnmJnlaVR01Zv20JCf1p//SWmtjjjlBzpCzAAAPRUlEQVQtyU1JXlhVTxpzzAZJTuhPP31frxkAAAAAAAAAAEhmdB3gQXh1kguTnFxVB6dXPtoryYHpPTbunePWz+2PNWbb7km+UVUX9o+5IcmWSfZO8vgkdyQ5urW2fOSA1tryqnppendpOq2qTktydZKDkzwpyY+TfHTshVtrt1XVMekVns6rqq8kWZzkqCSz+9u/+uD/KgAAAAAAAAAAYN0wqe7olIze1elJSU5Jr+D05iQ7Jzk5yT6ttUUP4DQXp1dKmpnkyCRvSfI3SVqSDyd5TGvt/JVc+2fp3e3pW0kOTfLGJLOS/GOSQ1b2CLrW2jeT7J/kgiTPS/LaJPcmeVOSF467AxQAAAAAAAAAALASk/GOTmmtLUzy0ge4tlay7er0ClIP5tqXJHnBah7z4yRHPJjrAQAAAAAAAAAAk/COTgAAAAAAAAAAwLpH0QkAAAAAAAAAABh6ik4AAAAAAAAAAMDQU3QCAAAAAAAAAACGnqITAAAAAAAAAAAw9BSdAAAAAAAAAACAoafoBAAAAAAAAAAADD1FJwAAAAAAAAAAYOgpOgEAAAAAAAAAAENP0QkAAAAAAAAAABh6ik4AAAAAAAAAAMDQU3QCAAAAAAAAAACGXrXWus7AGqiqRRtuuOFD5syZ03UUAAAAAAAAAAD4C3Pnzs3dd9+9uLW25ZqcR9Fpkquqq5JslmRBx1H4T7v1x3mdpgCA7ngvBGBd570QgHWd90IA1nXeCwH+0qOS3NZa23FNTqLoBGtZVV2UJK21PbrOAgBd8F4IwLrOeyEA6zrvhQCs67wXAkycaV0HAAAAAAAAAAAAuD+KTgAAAAAAAAAAwNBTdAIAAAAAAAAAAIaeohMAAAAAAAAAADD0FJ0AAAAAAAAAAIChV621rjMAAAAAAAAAAADcJ3d0AgAAAAAAAAAAhp6iEwAAAAAAAAAAMPQUnQAAAAAAAAAAgKGn6AQAAAAAAAAAAAw9RScAAAAAAAAAAGDoKToBAAAAAAAAAABDT9EJAAAAAAAAAAAYeopOsJZU1fZV9bmquraqllbVgqr6WFVt0XU2AJhoVbVlVb28qv6tqi6vqrur6taq+lFVvayqfN8JwDqnqo6uqtb/ennXeQBgEKpqv6r6elVd1/+c9LqqOruqjug6GwBMtKo6sv++98f+Z6RXVtXXqmqfrrMBTBXVWus6A0x6VbVzkguTbJPkW0nmJdkzyYFJLk3ylNbaou4SAsDEqqpjk3w6yXVJzk1ydZJtkzw3yawkX0/yguabTwDWEVX1iCS/TTI9ySZJjmmt/e9uUwHAxKqq45O8L8lNSb6T3s+IWyXZPcm5rbW3dRgPACZUVZ2U5G1JFiX5Znrvh49OclSSGUle0lr7UncJAaYGRSdYC6rqrCSHJnlda+2fx2z/SJI3JvlMa+3YrvIBwESrqoOSbJzk9NbaijHbH5rk50kekeT5rbWvdxQRAAamqirJ95LsmOQbSd4SRScApriqekGSU5N8P8lzW2u3j9u/Xmvt3k7CAcAE638Oek2SPyV5QmvtxjH7DkxyTpKrWms7dRQRYMrwCBFYQ1W1U3olpwVJPjlu93uS3Jnk6KraeMDRAGBgWmvntNb+/9iSU3/79Un+pT89YODBAKAbr0tyUJKXpvczIQBMaf3HlZ+U5K4kLxpfckoSJScAprgd0vvd+8/GlpySpLV2bpLbk2zdRTCAqUbRCdbcQf3x7JX8cvf2JD9OslGSvQcdDACGxMiH2cs6TQEAA1BVc5KcmOTjrbULus4DAAOyb3p3Mjwjyc1VdWRVHVdVr6+qfTrOBgCDcFmSe5LsWVVbjd1RVU9Lsml6dz0EYA3N6DoATAGz++P8Vey/LL07Pu2a5AcDSQQAQ6KqZiR5SX96ZpdZAGCi9d/3vpjk6iTv6DgOAAzSk/vjDUkuTvL4sTur6oL0Hmf+p0EHA4BBaK0trqrjknwkySVV9c0ki5LsnOSo9B5v/soOIwJMGYpOsOZm9cdbV7F/ZPvmA8gCAMPmxCSPS3JGa+2srsMAwAR7d5Ldkzy1tXZ312EAYIC26Y/HJrkqydOT/Cy9x/h8OMlhSb4WjzQHYAprrX2sqhYk+VySY8bsujzJKeMfaQfAg+PRdTDxqj+2TlMAwIBV1euSvDnJvCRHdxwHACZUVe2Z3l2cPtxa+0nXeQBgwKb3x0rvzk0/aK3d0Vr7fZLnJPljkv09xg6Aqayq3pbktCSnpHcnp42T7JHkyiRfrqp/6i4dwNSh6ARrbuSOTbNWsX+zcesAYMqrqtck+XiSS5Ic2Fpb3HEkAJgwYx5ZNz/JuzqOAwBduLk/Xtla+/XYHf27HI7c4XfPgaYCgAGpqgOSnJTk2621N7XWrmyt3dVauzi90u81Sd5cVTt1mRNgKlB0gjV3aX/cdRX7d+mP8weQBQA6V1VvSPKJJL9Lr+R0fceRAGCibZLez4RzkiypqjbyleQ9/TWf7W/7WGcpAWDijHxGessq9o8UoTYcQBYA6MIz++O543e01u5K8vP0fje/+yBDAUxFM7oOAFPAyDcsh1bVtNbaipEdVbVpkqckuTvJT7sIBwCDVFXHJTkxya+SHNJau6njSAAwCEuT/Osq9j0xvQ+yf5TeL4E91g6AqeiCJMuS7FJVM1tr94zb/7j+uGCgqQBgcNbvj1uvYv/I9vHvkQCsJnd0gjXUWrsiydlJHpXkNeN2vze95+9+obV254CjAcBAVdW70is5XZTkYCUnANYVrbW7W2svX9lXkm/3l/2f/ravdpkVACZC/+e/ryaZleTdY/dV1SFJDktya5IzB58OAAbih/3xFVW13dgdVfWM9G6MsCTJhYMOBjDVuKMTrB2vTu8bk5Or6uAkc5PsleTA9B5Z984OswHAhKuqv03yj0mWp/dD/euqavyyBa21UwYcDQAAgMF4U3qfib6zqp6W3iN6dkjynPR+VjymtbaqR9sBwGR3WpLvJ3l6krlV9W9Jrk/vEefPTFJJ/qG1tqi7iABTg6ITrAWttSuq6knp/YL38CRHJLkuyclJ3ttaW9xlPgAYgB374/Qkb1jFmvOTnDKQNAAAAAxUa+3GqtoryfHplZv2TnJ7ktOTfLC19tMu8wHARGqtraiqI9J7+ssL03sv3CjJ4iRnJDm5tXZ2hxEBpoxqrXWdAQAAAAAAAAAA4D5N6zoAAAAAAAAAAADA/VF0AgAAAAAAAAAAhp6iEwAAAAAAAAAAMPQUnQAAAAAAAAAAgKGn6AQAAAAAAAAAAAw9RScAAAAAAAAAAGDoKToBAAAAAAAAAABDT9EJAAAAAAAAAAAYeopOAAAAAAAAAADA0FN0AgAAAAAAAAAAhp6iEwAAAAAAAAAAMPQUnQAAAABgAlXVeVXVus4BAAAAMNkpOgEAAAAAAAAAAENP0QkAAAAAAAAAABh6ik4AAAAAAAAAAMDQU3QCAAAAYFKoqr2q6rSqur6q7qmqhVX1map6+Lh151VVq6r1q+qEqrqqqpZW1RVV9Z6qmrmK8x9cVWdW1eKqWlJV86vqxKqatYr1D6mq91fV76rqrqq6tap+3T9m45Wsn1FV76iqy/p5FlbVSavKAwAAAMCfq9Za1xkAAAAA4D5V1UuTfDbJ0iTfTrIwyS5JjkpyQ5K9W2tX99eel2T//ronJzktyb1Jnp1k5yTfSXJUG/PBWFW9Msmnk9yZ5GtJbkxyQJK9klyS5CmttVvGrN8xyblJdkhyUZLz0/tPhbsmeXqS2a21BePyfC3Jfkm+m+S2JEf0X8MprbWXrpW/KAAAAIApTNEJAAAAgKFWVbsm+V2Sq5Ps31q7Zsy+g5J8L8m3W2vP6W87L71i0WVJ9mqt3dzfvkF65aS9k7yktfbF/vYdksxPr0S1Z2tt3pjzfyrJq5J8trX2ijHbf5xk3yTvaK19cFzerZLc0VpbMi7PxUkOaa0t7m/fOMmvk+yYZLvW2vVr/JcFAAAAMIV5dB0AAAAAw+5VSdZL8vqxJackaa2dk96dm55VVZuOO+59IyWn/tolSd7en/7dmHUvTjIzySfGlpz63pnk9iRHV9X6SVJVe6RXcvpVkpPGh22t3TRSchrnuJGSU3/dnUm+nN5ndE9a2QsHAAAA4D/N6DoAAAAAANyPffrj/lX15JXs3ybJ9PQeG3fRmO3nr2TtD5MsS7L7mG1P7I/njF/cWru5qn6Z5GlJdkvvDkx793ef1Vpb8UBfRJJfrGTbwv64xWqcBwAAAGCdpOgEAAAAwLDbsj++9X7WbTJufsP4Ba215VW1KL1y1IhZ/fG6VZx3ZPvm48ZrVrJ2lVprt6xk87L+OH11zgUAAACwLlJ0AgAAAGDY3dofZ7XWbluN47ZNcvXYDVU1Pb3i1NjzjJz/oUl+v5LzPGzcupHC0narkQUAAACANTSt6wAAAAAAcD9+2h/3W83j9l/Jtv3S+89/vxyzbeTPB4xfXFWbJ/mvSZYkmTsuz2FV5fM1AAAAgAHxQQwAAAAAw+4TSe5N8tGq2nX8zqqaWVUrK0G9q6q2GLNugyQf7E8/P2bdl/rnf21VPXrcOd6XZLMkX2qtLU2S1tpFSS5MrwB13ErybNm/FgAAAABrkUfXAQAAADDUWmvzqurvknwuye+r6swk85Osl+SR6d2l6U9Jdht36Nz++tPSKzI9O8nOSU5P8sUx519QVW9I8skkF1fVqf3z7Z9knyTz8peFphcnOS/JB6rqef0/V5Jdkhzaz7JgzV89AAAAACMUnQAAAAAYeq21L1XVr5O8OcmB6ZWJ7kxybZLTknx1JYf9VZJ3JfnvSR6e5Jok/zPJia21Nu78n6qqy5O8JcnzkmyUZGGSDyX5QGvtlnHrr6qqJyZ5W5L/luTv03u83YIkH05y4xq/aAAAAAD+TI37TAcAAAAAJrWqOi/J/q216joLAAAAAGvPtK4DAAAAAAAAAAAA3B9FJwAAAAAAAAAAYOgpOgEAAAAAAAAAAEOvWmtdZwAAAAAAAAAAALhP7ugEAAAAAAAAAAAMPUUnAAAAAAAAAABg6Ck6AQAAAAAAAAAAQ0/RCQAAAAAAAAAAGHqKTgAAAAAAAAAAwNBTdAIAAAAAAAAAAIaeohMAAAAAAAAAADD0FJ0AAAAAAAAAAIChp+gEAAAAAAAAAAAMPUUnAAAAAAAAAABg6Ck6AQAAAAAAAAAAQ0/RCQAAAAAAAAAAGHqKTgAAAAAAAAAAwND7D37qSwsYuPpPAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 440, + "width": 1181 + }, + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 440, + "width": 1168 + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# summarize history for accuracy\n", + "plt.plot(history.history['categorical_accuracy'])\n", + "plt.plot(history.history['val_categorical_accuracy'])\n", + "plt.title('model accuracy')\n", + "plt.ylabel('accuracy')\n", + "plt.xlabel('epoch')\n", + "plt.legend(['train', 'test'], loc='upper left')\n", + "plt.show()\n", + "# summarize history for loss\n", + "plt.plot(history.history['loss'])\n", + "plt.plot(history.history['val_loss'])\n", + "plt.title('model loss')\n", + "plt.ylabel('loss')\n", + "plt.xlabel('epoch')\n", + "plt.legend(['train', 'test'], loc='upper left')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loading vocabulary...\n", + "loading model...\n" + ] + } + ], + "source": [ + "save_dir = \"./save/\"\n", + "\n", + "# Load vocabulary\n", + "print(\"loading vocabulary...\")\n", + "vocab_file = os.path.join(save_dir, \"words_vocab.pkl\")\n", + "\n", + "with open(os.path.join(save_dir, 'words_vocab.pkl'), 'rb') as f:\n", + " words, vocab, vocabulary_inv = cPickle.load(f)\n", + "\n", + "vocab_size = len(words)\n", + "\n", + "# Load the model\n", + "print(\"loading model...\")\n", + "model = load_model(save_dir + \"/\" + 'my_model_generate_sentences.h5')" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "def sample(preds, temperature=1):\n", + " preds = np.asarray(preds).astype('float64')\n", + " preds = np.log(preds) / temperature\n", + " exp_preds = np.exp(preds)\n", + " preds = exp_preds / np.sum(exp_preds)\n", + " probas = np.random.multinomial(1, preds, 1)\n", + " return np.argmax(probas)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Generating text with the following seed: \"336 305 310 358 310 327\"\n", + "\n" + ] + } + ], + "source": [ + "# Iniatate sentence\n", + "seed_sentences = \"327 336 305 310 358 310 344\"\n", + "generated = ''\n", + "sentence = []\n", + "for i in range (seq_length):\n", + " sentence.append(\"312\")\n", + "\n", + "seed = seed_sentences.split()\n", + "\n", + "for i in range(len(seed)):\n", + " sentence[seq_length-i-1]=seed[len(seed)-i-1]\n", + "\n", + "generated += ' '.join(sentence)\n", + "print('Generating text with the following seed: \"' + ' '.join(sentence) + '\"')\n", + "\n", + "print ()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now **generate** an example." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "words_number = 30000\n", + "# Generate the text\n", + "for i in range(words_number):\n", + " #create the vector\n", + " x = np.zeros((1, seq_length, vocab_size))\n", + " for t, word in enumerate(sentence):\n", + " x[0, t, vocab[word]] = 1.\n", + " #print(x.shape)\n", + "\n", + " #calculate next word\n", + " preds = model.predict(x, verbose=0)[0]\n", + " next_index = sample(preds, 0.2)\n", + " next_word = vocabulary_inv[next_index]\n", + "\n", + " #add the next word to the text\n", + " generated += \" \" + next_word\n", + " # shift the sentence by one, and and the next word at its end\n", + " sentence = sentence[1:] + [next_word]\n", + "\n", + "# print(generated)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "generated_notes_list = []\n", + "for single_generated in generated.split(\" \"):\n", + " generated_notes_list.append(int(single_generated))" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "np.asarray(generated_notes_list)\n", + "arr = np.asarray(generated_notes_list)\n", + "generated_data_frame = pd.DataFrame({'Koma53':arr})" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 436, + "width": 1164 + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "pd.value_counts(generated_data_frame['Koma53']).plot(kind='bar', title=\"Dist. of Generated Koma53\")" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 436, + "width": 1164 + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# pd.value_counts(df_hicaz_sarki['Mapped Koma53']).plot.bar()\n", + "pd.value_counts(df_hicaz_sarki['Mapped Koma53']).plot(kind='bar', title=\"Dist. of Original Koma53\")" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + " 336 6238\n", + " 327 5612\n", + " 349 4322\n", + " 344 3948\n", + " 358 3314\n", + " 322 3023\n", + " 310 1437\n", + " 340 1083\n", + " 305 908\n", + " 296 67\n", + " 367 49\n", + "-1 5\n", + "Name: Koma53, dtype: int64" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.value_counts(generated_data_frame['Koma53'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [conda env:turkishMusicGen] *", + "language": "python", + "name": "conda-env-turkishMusicGen-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}