5x5,7x7,9x9_patterns，Fig 6, Fig S22.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# this file find weights for 5x5, 7x7, 9x9 patterns\n",
    "# both for binary and non-binary patterns\n",
    "# chemical naming: AHL is the same as OC6, CHL is the same as OHC14"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from numpy.random import RandomState\n",
    "import numpy as np\n",
    "np.set_printoptions(precision=2,suppress=True)\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.optimize as opt\n",
    "from math import *\n",
    "\n",
    "from scipy.optimize import basinhopping,minimize,differential_evolution\n",
    "from scipy.stats import moment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# basis functions, phi_pos, phi_neg\n",
    "def transf_pos(x,K=2.0,y0=0.001,ym=1.5,n=2):\n",
    "    result=ym*((x/K)**n)/(1+(x/K)**n)+ym*y0\n",
    "    return result\n",
    "\n",
    "def transf_neg(x,K=2.0,y0=0.001,ym=1.5,n=2):\n",
    "    result=ym/(1+(x/K)**n)+y0*ym\n",
    "    return result\n",
    "\n",
    "#------------\n",
    "# added during revision\n",
    "\n",
    "def quant(data,levels):\n",
    "    results=[]\n",
    "    for i in range(0,len(data)):\n",
    "        ind=np.argmin(abs(data[i]-np.array(levels)))\n",
    "        results.append(levels[ind])\n",
    "    return results\n",
    "\n",
    "\n",
    "def wt_update(w,h,err_sign,levels):\n",
    "    # h is a hidden variable\n",
    "    w_=w.copy()\n",
    "    for i in range(0,len(w)):\n",
    "        indx_=levels.index(w[i])\n",
    "        \n",
    "        if err_sign[i]>0 and indx_<len(levels)-1:\n",
    "            if h[i]>levels[indx_+1]:\n",
    "                w_[i]=levels[indx_+1]\n",
    "                \n",
    "        if err_sign[i]<0 and indx_>0:\n",
    "            if h[i]<levels[indx_-1]:\n",
    "                w_[i]=levels[indx_-1]       \n",
    "    return w_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def noisy(vector):\n",
    "    result=[]\n",
    "    for i in range(0,len(vector)):\n",
    "        v=vector.copy()\n",
    "        v[i]=np.logical_not(vector[i]).astype(int)\n",
    "        #print(i,vector[i],v[i])\n",
    "        result.append(v)\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "zv_5x5=[1,1,1,0,0,\\\n",
    "        0,0,1,0,0,\\\n",
    "        0,0,1,0,0,\\\n",
    "        0,0,1,0,0,\\\n",
    "        0,0,1,1,1]\n",
    "\n",
    "zv_7x7=[1,1,1,1,0,0,0,\\\n",
    "        0,0,0,1,0,0,0,\\\n",
    "        0,0,0,1,0,0,0,\\\n",
    "        0,0,0,1,0,0,0,\\\n",
    "        0,0,0,1,0,0,0,\\\n",
    "        0,0,0,1,0,0,0,\\\n",
    "        0,0,0,1,1,1,1]\n",
    "\n",
    "zv_9x9=[1,1,1,1,1,0,0,0,0,\\\n",
    "        0,0,0,0,1,0,0,0,0,\\\n",
    "        0,0,0,0,1,0,0,0,0,\\\n",
    "        0,0,0,0,1,0,0,0,0,\\\n",
    "        0,0,0,0,1,0,0,0,0,\\\n",
    "        0,0,0,0,1,0,0,0,0,\\\n",
    "        0,0,0,0,1,0,0,0,0,\\\n",
    "        0,0,0,0,1,0,0,0,0,\\\n",
    "        0,0,0,0,1,1,1,1,1]    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "vv_5x5=[1,0,0,0,1,\\\n",
    "        1,0,0,0,1,\\\n",
    "        1,0,0,0,1,\\\n",
    "        0,1,0,1,0,\\\n",
    "        0,0,1,0,0]\n",
    "\n",
    "vv_7x7=[1,0,0,0,0,0,1,\\\n",
    "        1,0,0,0,0,0,1,\\\n",
    "        1,0,0,0,0,0,1,\\\n",
    "        1,0,0,0,0,0,1,\\\n",
    "        0,1,0,0,0,1,0,\\\n",
    "        0,0,1,0,1,0,0,\\\n",
    "        0,0,0,1,0,0,0]\n",
    "\n",
    "vv_9x9=[1,0,0,0,0,0,0,0,1,\\\n",
    "        1,0,0,0,0,0,0,0,1,\\\n",
    "        1,0,0,0,0,0,0,0,1,\\\n",
    "        1,0,0,0,0,0,0,0,1,\\\n",
    "        1,0,0,0,0,0,0,0,1,\\\n",
    "        0,1,0,0,0,0,0,1,0,\\\n",
    "        0,0,1,0,0,0,1,0,0,\\\n",
    "        0,0,0,1,0,1,0,0,0,\\\n",
    "        0,0,0,0,1,0,0,0,0]    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "nv_5x5=[0,0,0,0,0,\\\n",
    "        0,1,1,1,0,\\\n",
    "        1,0,0,0,1,\\\n",
    "        1,0,0,0,1,\\\n",
    "        1,0,0,0,1]\n",
    "\n",
    "nv_7x7=[0,0,0,0,0,0,0,\\\n",
    "        0,0,0,0,0,0,0,\\\n",
    "        0,1,1,1,1,1,0,\\\n",
    "        1,0,0,0,0,0,1,\\\n",
    "        1,0,0,0,0,0,1,\\\n",
    "        1,0,0,0,0,0,1,\\\n",
    "        1,0,0,0,0,0,1]\n",
    "\n",
    "nv_9x9=[0,0,0,0,0,0,0,0,0,\\\n",
    "        0,0,0,0,0,0,0,0,0,\\\n",
    "        0,0,1,1,1,1,1,0,0,\\\n",
    "        0,1,0,0,0,0,0,1,0,\\\n",
    "        1,0,0,0,0,0,0,0,1,\\\n",
    "        1,0,0,0,0,0,0,0,1,\\\n",
    "        1,0,0,0,0,0,0,0,1,\\\n",
    "        1,0,0,0,0,0,0,0,1,\\\n",
    "        1,0,0,0,0,0,0,0,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 5x5 pattern set\n",
    "zpatterns=([zv_5x5]+noisy(zv_5x5))\n",
    "vpatterns=([vv_5x5]+noisy(vv_5x5))\n",
    "npatterns=([nv_5x5]+noisy(nv_5x5))\n",
    "\n",
    "patterns_5x5=zpatterns+vpatterns+npatterns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 7x7\n",
    "zpatterns=([zv_7x7]+noisy(zv_7x7))\n",
    "vpatterns=([vv_7x7]+noisy(vv_7x7))\n",
    "npatterns=([nv_7x7]+noisy(nv_7x7))\n",
    "\n",
    "patterns_7x7=zpatterns+vpatterns+npatterns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 9x9\n",
    "zpatterns=([zv_9x9]+noisy(zv_9x9))\n",
    "vpatterns=([vv_9x9]+noisy(vv_9x9))\n",
    "npatterns=([nv_9x9]+noisy(nv_9x9))\n",
    "\n",
    "patterns_9x9=zpatterns+vpatterns+npatterns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define variables\n",
    "# for 5x5 patters, size=25, patterns=patterns_5x5\n",
    "# for 7x7 patters, size=49, patterns=patterns_7x7\n",
    "# for 9x9 patters, size=81, patterns=patterns_9x9\n",
    "# binary inputs\n",
    "\n",
    "size=81\n",
    "patterns=patterns_9x9\n",
    "\n",
    "target_L=0.0\n",
    "target_H=1.2\n",
    "lenth=int(len(patterns)/3)\n",
    "\n",
    "target_0=np.array([target_H]*lenth+[target_L]*lenth+[target_L]*lenth)\n",
    "target_1=np.array([target_L]*lenth+[target_H]*lenth+[target_L]*lenth)\n",
    "target_2=np.array([target_L]*lenth+[target_L]*lenth+[target_H]*lenth)\n",
    "\n",
    "target=target_0\n",
    "#----\n",
    "AHL_unit_level=33# uM \n",
    "transf_p=np.zeros(2)\n",
    "transf_n=np.zeros(2)\n",
    "\n",
    "# parameter numbers are approximated from values in Table 1\n",
    "transf_p[0]=transf_pos(x=0,K=50,y0=0.1,ym=1.0,n=0.45)\n",
    "transf_p[1]=transf_pos(x=AHL_unit_level,K=50,y0=0.1,ym=1.0,n=0.45)\n",
    "transf_n[0]=transf_neg(x=0,K=0.252,y0=0.341,ym=1,n=0.67)\n",
    "transf_n[1]=transf_neg(x=AHL_unit_level,K=0.252,y0=0.341,ym=1,n=0.67)\n",
    "\n",
    "phi_pos=np.array([transf_p]*size)\n",
    "phi_neg=np.array([transf_n]*size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rand_seq:  [0.65 0.83 0.4  0.58 0.49 0.46 0.51 0.61 0.64 0.72 0.65 0.81 0.52 0.93\n",
      " 0.42 0.8  0.65 0.74 0.48 0.52 0.88 0.98 0.59 0.82 0.93 0.94 0.45 0.42\n",
      " 0.5  0.93 0.46 0.65 0.97 0.72 0.82 0.59 0.81 0.9  0.41 0.85 0.99 0.85\n",
      " 0.57 0.87 0.46 0.67 0.95 0.58 0.57 0.48 0.41 0.81 0.53 0.56 0.69 0.43\n",
      " 0.74 0.49 0.75 0.82 0.46 0.65 0.82 0.65 0.43 0.72 0.8  0.71 0.97 0.75\n",
      " 0.94 0.48 0.48 0.88 0.64 0.5  0.96 0.61 0.85 0.84 0.93]\n"
     ]
    }
   ],
   "source": [
    "#for non-binary input\n",
    "\n",
    "rnd_state=RandomState(1)\n",
    "rand_seq=0.6*rnd_state.random_sample(size)+0.4\n",
    "print('rand_seq: ',rand_seq)\n",
    "\n",
    "phi_pos_ct=np.zeros(shape=phi_pos.shape)\n",
    "phi_neg_ct=np.zeros(shape=phi_neg.shape)\n",
    "\n",
    "phi_pos_ct.T[0]=phi_pos.T[0]\n",
    "phi_pos_ct.T[1]=phi_pos.T[1]*rand_seq\n",
    "\n",
    "phi_neg_ct.T[0]=phi_neg.T[0]\n",
    "phi_neg_ct.T[1]=phi_neg.T[1]*(2-rand_seq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# update for revision\n",
    "def examine_output(w,P,phi_pos,phi_neg):\n",
    "    y_n=np.zeros(len(P))\n",
    "    w_ph_=np.zeros(len(P))\n",
    "\n",
    "    for n in range(0,len(P)):\n",
    "        pttn=np.array(P[n])\n",
    "        \n",
    "        phi_nw=get_phinw(w,pttn,phi_pos,phi_neg)\n",
    "                \n",
    "        w_ph_[n]=np.dot(w,phi_nw)/len(w)\n",
    "        y_n[n]=activate(w_ph_[n])\n",
    "        \n",
    "    return y_n,w_ph_\n",
    "\n",
    "def output_fcn(pttn,fcn):\n",
    "    \n",
    "    CHL_levels=np.zeros(len(pttn))\n",
    "    for i in range(0,len(pttn)):\n",
    "        CHL_levels[i]=fcn[i][pttn[i]]\n",
    "        \n",
    "    return CHL_levels\n",
    "\n",
    "def activate(x):\n",
    "    K,y0,ym_x,n= 1.10292919e+03,1.94584842e-02,2.15950963e+04,2.33038211e+00\n",
    "    result=transf_pos(x.sum(),K=K,y0=y0/1e4,ym=2.0,n=n).sum()\n",
    "    return result\n",
    "\n",
    "def get_phinw(w,pttn,phi_pos,phi_neg):\n",
    "    # conitnuous means wether negative weights are allowed to vary\n",
    "    phi_nw=(1.0+w/np.sqrt(w**2))/2*output_fcn(pttn,phi_pos)+\\\n",
    "            (w/np.sqrt(w**2)-1)/2*output_fcn(pttn,phi_neg)\n",
    "\n",
    "    return phi_nw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# update continous weights\n",
    "# cont_ by default equals False\n",
    "def train_step1(target,size,eta,phi_pos,phi_neg,pos=False,cont_=False):\n",
    "    rndstate=RandomState(1)\n",
    "    \n",
    "    if cont_==True or pos==True:\n",
    "        h=rndstate.random_sample(size)\n",
    "    else:\n",
    "        h=rndstate.random_sample(size)*(-10)\n",
    "        \n",
    "    print('h: ',h)\n",
    "    r=size\n",
    "\n",
    "    w_series=[]\n",
    "    y_series=[]\n",
    "    delta_series=[]\n",
    "\n",
    "    for it in range(0,300):\n",
    "        y_n=np.zeros(len(patterns))\n",
    "        g_n=np.zeros(len(patterns))\n",
    "        delta_err=np.zeros(size)\n",
    "        phi_n=[]\n",
    "      \n",
    "        w=h.copy()   \n",
    "        if cont_==False:\n",
    "            w[w<0]=-735\n",
    "        \n",
    "        if pos==True:\n",
    "            w[w<0]=0.1\n",
    "            \n",
    "        for n in range(0,len(patterns)):\n",
    "            pttn=np.array(patterns[n])            \n",
    "            phi_nw=get_phinw(w,pttn,phi_pos,phi_neg)\n",
    "\n",
    "            # h is the hidden variable\n",
    "            g_n[n]=np.dot(w,phi_nw)/r\n",
    "            y_n[n]=activate(g_n[n])            \n",
    "            \n",
    "            tmp=(y_n[n]-target[n])\n",
    "            tmp=min(target_H,tmp)\n",
    "            phi_nw_pos=(1.0+w/np.sqrt(w**2))/2*output_fcn(pttn,phi_pos) \n",
    "            delta_err+=tmp*phi_nw\n",
    "\n",
    "        w_series.append(w)\n",
    "        h=h-eta*delta_err \n",
    "        y_series.append(y_n)\n",
    "        delta_series.append(delta_err)\n",
    "        \n",
    "    return w,y_series,w_series,delta_series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# update weight in discrete levels\n",
    "# all weights initial values are negative weights\n",
    "def train_step1_dt(target,size,eta,phi_pos,phi_neg,wt_levels):\n",
    "    h=np.ones(size)*wt_levels[0]\n",
    "    w=h.copy()\n",
    "    r=size\n",
    "\n",
    "    w_series=[]\n",
    "    y_series=[]\n",
    "    delta_series=[]\n",
    "        \n",
    "    for it in range(0,300):\n",
    "        y_n=np.zeros(len(patterns))\n",
    "        g_n=np.zeros(len(patterns))\n",
    "        delta_err=np.zeros(size)\n",
    "        for n in range(0,len(patterns)):\n",
    "            pttn=np.array(patterns[n])        \n",
    "            phi_nw=get_phinw(w,pttn,phi_pos,phi_neg)\n",
    "\n",
    "            g_n[n]=np.dot(w,phi_nw)/r\n",
    "            y_n[n]=activate(g_n[n])            \n",
    "            tmp=(y_n[n]-target[n])\n",
    "            tmp=min(target_H,tmp)            \n",
    "            delta_err+=tmp*phi_nw\n",
    "            \n",
    "        y_series.append(y_n)\n",
    "        w_series.append(w)\n",
    "        delta_series.append(delta_err)\n",
    "        h=h-eta*delta_err\n",
    "        w=wt_update(w,h,-np.sign(delta_err),wt_levels)\n",
    "        \n",
    "    p_=(2*np.array(y_series)-target_H)*(2*target-target_H)\n",
    "    pm_=np.var(p_,axis=1)*10+(target_H**2-np.mean(p_,axis=1))\n",
    "    \n",
    "    w=w_series[np.argmin(pm_)]\n",
    "    y=y_series[np.argmin(pm_)]\n",
    "    \n",
    "    return w,y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train using discrete weights, binary input\n",
    "wtlevels_=([-735.0,735,1906,2679.0,5500,10000])\n",
    "\n",
    "# discrete weights\n",
    "#w0_5x5,y0_5x5=train_step1_dt(target_0,25,eta=20,phi_pos=phi_pos,phi_neg=phi_neg,wt_levels=wtlevels_)\n",
    "#w1_5x5,y1_5x5=train_step1_dt(target_1,25,eta=20,phi_pos=phi_pos,phi_neg=phi_neg,wt_levels=wtlevels_)\n",
    "#w2_5x5,y2_5x5=train_step1_dt(target_2,25,eta=20,phi_pos=phi_pos,phi_neg=phi_neg,wt_levels=wtlevels_)\n",
    "\n",
    "#w0_7x7,y0_7x7=train_step1_dt(target_0,49,eta=20,phi_pos=phi_pos,phi_neg=phi_neg,wt_levels=wtlevels_)\n",
    "#w1_7x7,y1_7x7=train_step1_dt(target_1,49,eta=20,phi_pos=phi_pos,phi_neg=phi_neg,wt_levels=wtlevels_)\n",
    "#w2_7x7,y2_7x7=train_step1_dt(target_2,49,eta=20,phi_pos=phi_pos,phi_neg=phi_neg,wt_levels=wtlevels_)\n",
    "\n",
    "w0_9x9,y0_9x9=train_step1_dt(target_0,81,eta=20,phi_pos=phi_pos,phi_neg=phi_neg,wt_levels=wtlevels_)\n",
    "w1_9x9,y1_9x9=train_step1_dt(target_1,81,eta=20,phi_pos=phi_pos,phi_neg=phi_neg,wt_levels=wtlevels_)\n",
    "w2_9x9,y2_9x9=train_step1_dt(target_2,81,eta=20,phi_pos=phi_pos,phi_neg=phi_neg,wt_levels=wtlevels_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
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     "metadata": {},
     "output_type": "execute_result"
    },
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\n",
      "text/plain": [
       "<Figure size 1044x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# demo example, for Fig 6a\n",
    "ymax=1.3\n",
    "\n",
    "fig=plt.figure(figsize=(14.5,4))\n",
    "#=======\n",
    "ax=fig.add_subplot(131)\n",
    "\n",
    "line0,=ax.plot(np.arange(0,len(y0_5x5)),y0_5x5,marker='.',lw=0.5,color='royalblue',label='$\\\\vec{w_0}$')\n",
    "line1,=ax.plot(np.arange(0,len(y1_5x5)),y1_5x5,marker='.',lw=0.5,color='mediumseagreen',label='$\\\\vec{w_1}$')\n",
    "line2,=ax.plot(np.arange(0,len(y2_5x5)),y2_5x5,marker='.',lw=0.5,color='orange',label='$\\\\vec{w_2}$')\n",
    "ax.set_ylim([0,ymax])\n",
    "ax.tick_params(direction='in',size=12.5)\n",
    "ax.set_yticks([0,0.2,0.5,0.8,1.0])\n",
    "ax.set_xlabel('5x5 patterns',fontsize=14)\n",
    "ax.legend(loc='upper left',ncol=3,fontsize=13)\n",
    "ax.set_xticks([0,26,52])\n",
    "#=======\n",
    "ax=fig.add_subplot(132)\n",
    "\n",
    "ax.plot(np.arange(0,len(y0_7x7)),y0_7x7,marker='.',lw=0.5,color='royalblue')\n",
    "ax.plot(np.arange(0,len(y1_7x7)),y1_7x7,marker='.',lw=0.5,color='mediumseagreen')\n",
    "ax.plot(np.arange(0,len(y2_7x7)),y2_7x7,marker='.',lw=0.5,color='orange')\n",
    "ax.set_ylim([0,ymax])\n",
    "ax.tick_params(direction='in',size=12.5)\n",
    "ax.set_yticks([0,0.2,0.5,0.8,1.0])\n",
    "ax.set_xlabel('7x7 patterns',fontsize=14)\n",
    "ax.set_xticks([0,50,100])\n",
    "\n",
    "#=======\n",
    "ax=fig.add_subplot(133)\n",
    "\n",
    "ax.plot(np.arange(0,len(y0_9x9)),y0_9x9,marker='.',lw=0.5,color='royalblue')\n",
    "ax.plot(np.arange(0,len(y1_9x9)),y1_9x9,marker='.',lw=0.5,color='mediumseagreen')\n",
    "ax.plot(np.arange(0,len(y2_9x9)),y2_9x9,marker='.',lw=0.5,color='orange')\n",
    "ax.set_ylim([0,ymax])\n",
    "ax.tick_params(direction='in',size=12.5)\n",
    "ax.set_yticks([0,0.2,0.5,0.8,1.0])\n",
    "ax.set_xlabel('9x9 patterns',fontsize=14)\n",
    "ax.set_xticks([0,82,164])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train using discrete weights, binary input\n",
    "wtlevels_=([-735.0,735,1906,2679.0,5500,10000])\n",
    "\n",
    "# discrete weights\n",
    "#w0_ct5x5,y0_5x5_ct=train_step1_dt(target_0,25,eta=20,phi_pos=phi_pos_ct,phi_neg=phi_neg_ct,wt_levels=wtlevels_)\n",
    "#w1_ct5x5,y1_5x5_ct=train_step1_dt(target_1,25,eta=20,phi_pos=phi_pos_ct,phi_neg=phi_neg_ct,wt_levels=wtlevels_)\n",
    "#w2_ct5x5,y2_5x5_ct=train_step1_dt(target_2,25,eta=20,phi_pos=phi_pos_ct,phi_neg=phi_neg_ct,wt_levels=wtlevels_)\n",
    "\n",
    "#w0_ct7x7,y0_7x7_ct=train_step1_dt(target_0,49,eta=20,phi_pos=phi_pos_ct,phi_neg=phi_neg_ct,wt_levels=wtlevels_)\n",
    "#w1_ct7x7,y1_7x7_ct=train_step1_dt(target_1,49,eta=20,phi_pos=phi_pos_ct,phi_neg=phi_neg_ct,wt_levels=wtlevels_)\n",
    "#w2_ct7x7,y2_7x7_ct=train_step1_dt(target_2,49,eta=20,phi_pos=phi_pos_ct,phi_neg=phi_neg_ct,wt_levels=wtlevels_)\n",
    "\n",
    "w0_ct9x9,y0_9x9_ct=train_step1_dt(target_0,81,eta=20,phi_pos=phi_pos_ct,phi_neg=phi_neg_ct,wt_levels=wtlevels_)\n",
    "w1_ct9x9,y1_9x9_ct=train_step1_dt(target_1,81,eta=20,phi_pos=phi_pos_ct,phi_neg=phi_neg_ct,wt_levels=wtlevels_)\n",
    "w2_ct9x9,y2_9x9_ct=train_step1_dt(target_2,81,eta=20,phi_pos=phi_pos_ct,phi_neg=phi_neg_ct,wt_levels=wtlevels_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.axis.XTick at 0x1220eb280>,\n",
       " <matplotlib.axis.XTick at 0x1220eb250>,\n",
       " <matplotlib.axis.XTick at 0x1220e1d90>]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1044x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# demo Fig 6b\n",
    "ymax=1.3\n",
    "\n",
    "fig=plt.figure(figsize=(14.5,4))\n",
    "#=======\n",
    "ax=fig.add_subplot(131)\n",
    "\n",
    "line0,=ax.plot(np.arange(0,len(y0_5x5_ct)),y0_5x5_ct,marker='.',lw=0.5,color='royalblue',label='$\\\\vec{w_0}$')\n",
    "line1,=ax.plot(np.arange(0,len(y1_5x5_ct)),y1_5x5_ct,marker='.',lw=0.5,color='mediumseagreen',label='$\\\\vec{w_1}$')\n",
    "line2,=ax.plot(np.arange(0,len(y2_5x5_ct)),y2_5x5_ct,marker='.',lw=0.5,color='orange',label='$\\\\vec{w_2}$')\n",
    "ax.set_ylim([0,ymax])\n",
    "ax.tick_params(direction='in',size=12.5)\n",
    "ax.set_yticks([0,0.2,0.5,0.8,1.0])\n",
    "ax.set_xlabel('5x5 patterns',fontsize=14)\n",
    "ax.legend(loc='upper left',ncol=3,fontsize=13)\n",
    "ax.set_xticks([0,26,52])\n",
    "#=======\n",
    "ax=fig.add_subplot(132)\n",
    "\n",
    "ax.plot(np.arange(0,len(y0_7x7_ct)),y0_7x7_ct,marker='.',lw=0.5,color='royalblue')\n",
    "ax.plot(np.arange(0,len(y1_7x7_ct)),y1_7x7_ct,marker='.',lw=0.5,color='mediumseagreen')\n",
    "ax.plot(np.arange(0,len(y2_7x7_ct)),y2_7x7_ct,marker='.',lw=0.5,color='orange')\n",
    "ax.set_ylim([0,ymax])\n",
    "ax.tick_params(direction='in',size=12.5)\n",
    "ax.set_yticks([0,0.2,0.5,0.8,1.0])\n",
    "ax.set_xlabel('7x7 patterns',fontsize=14)\n",
    "ax.set_xticks([0,50,100])\n",
    "\n",
    "\n",
    "#=======\n",
    "ax=fig.add_subplot(133)\n",
    "\n",
    "ax.plot(np.arange(0,len(y0_9x9_ct)),y0_9x9_ct,marker='.',lw=0.5,color='royalblue')\n",
    "ax.plot(np.arange(0,len(y1_9x9_ct)),y1_9x9_ct,marker='.',lw=0.5,color='mediumseagreen')\n",
    "ax.plot(np.arange(0,len(y2_9x9_ct)),y2_9x9_ct,marker='.',lw=0.5,color='orange')\n",
    "ax.set_ylim([0,ymax])\n",
    "ax.tick_params(direction='in',size=12.5)\n",
    "ax.set_yticks([0,0.2,0.5,0.8,1.0])\n",
    "ax.set_xlabel('9x9 patterns',fontsize=14)\n",
    "ax.set_xticks([0,82,164])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "wt_num_5x5=[len(np.unique(w0_5x5)),len(np.unique(w1_5x5)),len(np.unique(w2_5x5))]\n",
    "wt_num_7x7=[len(np.unique(w0_7x7)),len(np.unique(w1_7x7)),len(np.unique(w2_7x7))]\n",
    "wt_num_9x9=[len(np.unique(w0_9x9)),len(np.unique(w1_9x9)),len(np.unique(w2_9x9))]\n",
    "\n",
    "wt_num_ct5x5=[len(np.unique(w0_ct5x5)),len(np.unique(w1_ct5x5)),len(np.unique(w2_ct5x5))]\n",
    "wt_num_ct7x7=[len(np.unique(w0_ct7x7)),len(np.unique(w1_ct7x7)),len(np.unique(w2_ct7x7))]\n",
    "wt_num_ct9x9=[len(np.unique(w0_ct9x9)),len(np.unique(w1_ct9x9)),len(np.unique(w2_ct9x9))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '$9\\\\times 9$ patterns')"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 612x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# demo Fig S22\n",
    "fig=plt.figure(figsize=(8.5,3))\n",
    "#------------\n",
    "ax=fig.add_subplot(131)\n",
    "\n",
    "ax.bar(x=np.linspace(0,len(wt_num_5x5)-1,len(wt_num_5x5)),height=wt_num_5x5,width=0.35,alpha=0.8,color='cornflowerblue',label='binary')\n",
    "ax.bar(x=np.linspace(0,len(wt_num_ct5x5)-1,len(wt_num_ct5x5))+0.4,height=wt_num_ct5x5,width=0.35,alpha=0.8,color='coral',label='nonbinary')\n",
    "\n",
    "ax.tick_params(direction='in',size=12)\n",
    "\n",
    "ax.set_yticks([3,5,6])\n",
    "ax.set_ylim([0,8])\n",
    "\n",
    "ax.set_xlim([-0.5,3])\n",
    "ax.set_xticks([0.25,1.25,2.25])\n",
    "ax.set_xticklabels(['$\\\\vec{w}_0$','$\\\\vec{w}_1$','$\\\\vec{w}_2$'],fontsize=12)\n",
    "\n",
    "ax.set_title('$5\\\\times 5$ patterns',fontsize=13)\n",
    "\n",
    "ax.set_ylabel('number of\\ndistinct weights levels',fontsize=12)\n",
    "#-------------------\n",
    "ax=fig.add_subplot(132)\n",
    "\n",
    "ax.bar(x=np.linspace(0,len(wt_num_7x7)-1,len(wt_num_7x7)),height=wt_num_7x7,width=0.35,alpha=0.8,color='cornflowerblue',label='binary patterns')\n",
    "ax.bar(x=np.linspace(0,len(wt_num_ct7x7)-1,len(wt_num_ct7x7))+0.4,height=wt_num_ct7x7,width=0.35,alpha=0.8,color='coral',label='nonbinary patterns')\n",
    "\n",
    "ax.tick_params(direction='in',size=12)\n",
    "\n",
    "ax.set_xlim([-0.5,3])\n",
    "ax.set_xticks([0.25,1.25,2.25])\n",
    "ax.set_xticklabels(['$\\\\vec{w}_0$','$\\\\vec{w}_1$','$\\\\vec{w}_2$'],fontsize=12)\n",
    "\n",
    "ax.set_yticks([])\n",
    "ax.set_ylim([0,8])\n",
    "\n",
    "ax.set_title('$7\\\\times 7$ patterns',fontsize=13)\n",
    "\n",
    "#--------------------\n",
    "ax=fig.add_subplot(133)\n",
    "\n",
    "ax.bar(x=np.linspace(0,len(wt_num_9x9)-1,len(wt_num_9x9)),height=wt_num_9x9,width=0.35,alpha=0.8,color='cornflowerblue',label='binary')\n",
    "ax.bar(x=np.linspace(0,len(wt_num_ct9x9)-1,len(wt_num_ct9x9))+0.4,height=wt_num_ct9x9,width=0.35,alpha=0.8,color='coral',label='non-binary')\n",
    "\n",
    "ax.tick_params(direction='in',size=12)\n",
    "ax.legend(loc='upper left',fontsize=12)\n",
    "\n",
    "ax.set_xlim([-0.5,3])\n",
    "ax.set_xticks([0.25,1.25,2.25])\n",
    "ax.set_xticklabels(['$\\\\vec{w}_0$','$\\\\vec{w}_1$','$\\\\vec{w}_2$'],fontsize=12)\n",
    "\n",
    "ax.set_yticks([])\n",
    "ax.set_ylim([0,8])\n",
    "\n",
    "ax.set_title('$9\\\\times 9$ patterns',fontsize=13)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}