FINAL_SPLICE.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib.pyplot import figure\n",
    "from sklearn.metrics import f1_score\n",
    "from scipy.sparse import csr_matrix\n",
    "from numpy import count_nonzero\n",
    "import matplotlib.pyplot as plt\n",
    "from numpy import linalg as LA\n",
    "import collections, numpy\n",
    "from numpy import array\n",
    "from time import time\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scipy.io\n",
    "import datetime\n",
    "import random\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "mushrooms = scipy.io.loadmat('C:\\\\Users\\\\Hunor\\\\Documents\\\\Moj Master rad\\\\Podaci za testiranje NKNKJTO\\\\Splice.mat')\n",
    "train = pd.DataFrame(mushrooms['Xtrain'].toarray())\n",
    "\n",
    "test = pd.DataFrame(mushrooms['Xtest'].toarray()) \n",
    "\n",
    "#labels = pd.DataFrame(mushrooms['ztrain'].toarray())\n",
    "labels = pd.DataFrame(mushrooms['ztrain'])\n",
    "labels = labels[0]\n",
    "#labels = np.array(labels[0])\n",
    "\n",
    "#labels_test = pd.DataFrame(mushrooms['ztest'].toarray())\n",
    "labels_test = pd.DataFrame(mushrooms['ztest'])\n",
    "labels_test = labels_test[0]\n",
    "#labels_test = np.array(labels_test[0])\n",
    "\n",
    "np.random.seed(11)\n",
    "x0 = np.random.uniform(0,1,train.shape[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3175"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape[0]+test.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def aimh(N_k):\n",
    "    return (N_max-N_k)/N_max"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def hinge_loss(x,N):\n",
    "    prediction = train.iloc[:N,:].dot(x)\n",
    "    maximums = np.maximum(0,1-labels[:N]*prediction)\n",
    "    return l/2*LA.norm(x)**2+1/N*maximums.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def hl_der(x,N):\n",
    "    y=train.iloc[:N,:].dot(x)*labels[:N]\n",
    "    maximumi= 1-y\n",
    "    izvodi_svi = (train.iloc[:N,:].T*labels[:N]).T\n",
    "    izvodi_veci = izvodi_svi[maximumi>0]\n",
    "    izvodi_nula = izvodi_svi[maximumi==0]\n",
    "    beta=np.random.uniform(0,1,np.size(izvodi_nula))\n",
    "    if len(izvodi_nula)!=0:\n",
    "        return np.array(l*x-izvodi_veci.sum()/N-np.dot(izvodi_nula,beta)/N)\n",
    "    elif len(izvodi_nula)==0:\n",
    "        return np.array(l*x-(izvodi_veci.sum())/N)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def new_sample(N_k): #working\n",
    "    return math.ceil(N_max*(1-r*aimh(N_k)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def merit_function(x_k, N_k, theta_k):\n",
    "    return theta_k*hinge_loss(x_k, N_k)+(1-theta_k)*aimh(N_k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_theta(x, N_tilda, N, theta):\n",
    "    sum1 = merit_function(x, N_tilda, theta)-merit_function(x, N, theta)\n",
    "    sum2 = (1-r)/2*(aimh(N_tilda)-aimh(N))\n",
    "    if sum1 <= sum2:\n",
    "        #print(\"Theta - Same\")\n",
    "        return theta\n",
    "    else:\n",
    "        sum3 = (1+r)*(aimh(N)-aimh(N_tilda))\n",
    "        sum4 = hinge_loss(x, N_tilda)-hinge_loss(x, N)+aimh(N)-aimh(N_tilda)\n",
    "        #print(\"Theta - Upgrade\")\n",
    "        return sum3/(2*sum4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def model_Y(x, p, N, B):\n",
    "    return np.matmul(np.matmul(p,np.linalg.inv(B)),p)/2 + sup_gp(x, p, N)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sup_gp(x, p, N):\n",
    "    sum1 = 0\n",
    "    sum2 = 0\n",
    "    for i in range(N):\n",
    "        check = 1-labels[i]*np.matmul(train.iloc[i,:],x)\n",
    "        if check == 0:\n",
    "            inf = labels[i]*np.matmul(train.iloc[i,:],p)\n",
    "            if inf < 0:\n",
    "                sum1 += inf\n",
    "        if check > 0:\n",
    "            sum2 += labels[i]*train.iloc[i,:]\n",
    "    return np.matmul(l*x - (sum2/N),p) - sum1/N"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def argsup_gp(x, p, N): #working\n",
    "    sum1 = np.zeros(x.shape)\n",
    "    sum2 = np.zeros(x.shape)\n",
    "    for i in range(N):\n",
    "        check = 1-labels[i]*np.matmul(train.iloc[i,:],x) \n",
    "        if check > 0:\n",
    "            sum1 += labels[i]*train.iloc[i][:]\n",
    "        if check == 0:\n",
    "            if labels[i]*np.matul(train.iloc[i,:],p) < 0:\n",
    "                sum2 += lables[i]*train.iloc[i][:]           \n",
    "    return np.array(l*x-sum1/N-sum2/N)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def descentDirection(g0_tilda, e, i_max, B, x, N): #working\n",
    "    G_tilda = []\n",
    "    G_bar = []\n",
    "    P = []\n",
    "    E = []\n",
    "    \n",
    "    #Step 0\n",
    "    g0_bar = g0_tilda\n",
    "    G_bar.append(g0_bar)\n",
    "    G_tilda.append(g0_tilda)\n",
    "    P.append(np.matmul(-B, G_tilda[0]))\n",
    "    \n",
    "    #Step 1\n",
    "    G_tilda.append(argsup_gp(x, P[0], N))\n",
    "        \n",
    "    #Step 2\n",
    "    E.append(np.matmul(P[0],G_tilda[1])-np.matmul(P[0],G_bar[0]))\n",
    "    \n",
    "    #Step 3\n",
    "    i = 0\n",
    "    while (np.matmul(G_tilda[i+1],P[i])>0 or E[0]>e) and E[i]>0 and i<i_max:\n",
    "        first = np.matmul(G_bar[i]-G_tilda[i+1],np.matmul(B,G_bar[i]))\n",
    "        second = np.matmul(G_bar[i]-G_tilda[i+1],np.matmul(B,G_bar[i]-G_tilda[i+1]))\n",
    "        ni = min(1,first/second)\n",
    "        G_bar.append((1-ni)*G_bar[i]+ni*G_tilda[i+1])\n",
    "        P.append((1-ni)*P[i]-ni*np.matmul(B,G_tilda[i+1]))\n",
    "        G_tilda.append(argsup_gp(x, P[i+1], N))\n",
    "        e_list = []\n",
    "        j = 0\n",
    "        while j <= i+1:\n",
    "            sum1 = np.matmul(P[j],G_tilda[j+1])\n",
    "            sum2 = np.matmul(P[j],G_bar[j])+np.matmul(P[i+1],G_bar[i+1])\n",
    "            e_list.append(sum1-sum2/2)\n",
    "            j += 1\n",
    "        E.append(min(e_list))\n",
    "        i += 1\n",
    "        \n",
    "    #Step 4\n",
    "    list_p = []\n",
    "    j = 0\n",
    "    while j <= i:\n",
    "        list_p.append(model_Y(x, P[j], N, B))\n",
    "        j += 1\n",
    "    p_final = P[np.argmin(list_p)]\n",
    "    \n",
    "    #STEP5\n",
    "    if sup_gp(x, p_final, N) < 0:\n",
    "        #print(\"Iteration in finding P is %s\" %i)\n",
    "        return p_final\n",
    "    else:\n",
    "        print(\"!!! FAILED TO FIND DESCENT DIRECTION!!!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def check1(x, alpha, p, N_trial, N_tilda):\n",
    "    sum1 = hinge_loss(x+alpha*p, N_trial)\n",
    "    sum2 = hinge_loss(x, N_tilda)\n",
    "    sum3 = -gama*alpha*(LA.norm(p,2)**2)\n",
    "    return (sum1-sum2)<=sum3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def check2(N_trial, N_tilda, alpha, p):\n",
    "    sum1 = aimh(N_trial)\n",
    "    sum2 = aimh(N_tilda)\n",
    "    sum3 = gama_k*alpha*alpha*(LA.norm(p,2)**2)\n",
    "    return sum1<=(sum2+sum3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def check3(x, alpha, p, N_trial, theta, N_start, N_tilda):\n",
    "    sum1 = merit_function(x+alpha*p, N_trial, theta)\n",
    "    sum2 = merit_function(x, N_start, theta)\n",
    "    sum3 = (1-r)*(aimh(N_tilda)-aimh(N_start))/2\n",
    "    return (sum1-sum2)<=sum3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def BFGS(B, g_new, g_old, x_new, x_old):\n",
    "    I = np.identity(g_new.size)\n",
    "    y = g_new-g_old\n",
    "    s = x_new-x_old\n",
    "    sT = np.reshape(s,(1,-1)) # Dimension: 1xN\n",
    "    s = np.reshape(s,(-1,1)) # Dimension: Nx1\n",
    "    yT = np.reshape(y,(1,-1))\n",
    "    y = np.reshape(y,(-1,1))\n",
    "    ro = 1/(yT@s)\n",
    "    sum1 = I-ro*(s@yT)\n",
    "    sum2 = I-ro*(y@sT)\n",
    "    sum3 = ro*(s@sT)\n",
    "    return (np.matmul(np.matmul(sum1,B),sum2)+sum3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def theta_k(theta):\n",
    "    return N_max*theta/(1-theta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def p_N_trial(N_start, N_tilda, theta, theta_k, alpha, p ,x):\n",
    "    sum1 = N_start + ((1-r)/2)*(N_tilda-N_start)/(1-theta)\n",
    "    sum2 = theta_k*(gama*alpha*(LA.norm(p,2)**2) - hinge_loss(x, N_tilda) + hinge_loss(x, N_start))\n",
    "    return round(math.floor(sum1-sum2))\n",
    "    #return sum1, sum2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def first_N_trial(N_start, N_tilda, theta, theta_k, alpha, x):\n",
    "    sum1 = N_start+(1-r)/2*(N_tilda-N_start)/(1-theta)\n",
    "    sum2 = theta_k*(gama*alpha-hinge_loss(x, N_tilda)+hinge_loss(x, N_start))\n",
    "    return math.ceil(sum1-sum2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def candidates(N_trial, N_tilda):\n",
    "    storage = []\n",
    "    storage.append(int(N_trial))\n",
    "    storage.append(int(math.ceil((N_trial+N_tilda)/2)))\n",
    "    storage.append(N_tilda)\n",
    "    return storage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def scores(k, x):\n",
    "    true_pos = 0\n",
    "    true_neg = 0\n",
    "    false_pos = 0\n",
    "    false_neg = 0\n",
    "    correct = 0\n",
    "    for i in range(test.shape[0]):\n",
    "        value = np.matmul(test.iloc[i,:], x)\n",
    "        if value > 0 and labels_test[i] > 0:\n",
    "            true_pos += 1\n",
    "        if value < 0 and labels_test[i] < 0:\n",
    "            true_neg += + 1\n",
    "        if value < 0 and labels_test[i] > 0:\n",
    "            false_pos += + 1\n",
    "        if value > 0 and labels_test[i] < 0:\n",
    "            false_neg += + 1\n",
    "        if value*labels_test[i]>0:\n",
    "            correct += 1      \n",
    "    F1 = round(true_pos/(true_pos + (false_pos+false_neg)/2),3)    \n",
    "    Accuracy = round(correct / test.shape[0], 3)\n",
    "    print(\"Iteration: %s | Accuracy: %s | F1: %s\" %(k, F1, Accuracy))\n",
    "    return F1, Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def backtracking_IRNS(B, x, N_trial, N_tilda, N_start, theta):\n",
    "    alpha_start = 0.5\n",
    "    degree = 0\n",
    "    g0 = hl_der(x, N_trial[0])\n",
    "    g1 = hl_der(x, N_trial[1])\n",
    "    g2 = hl_der(x, N_trial[2])\n",
    "    \n",
    "    p0 = descentDirection(g0, e, i_max, B, x, N_trial[0])\n",
    "    #print(\"P0 done\")\n",
    "    p1 = descentDirection(g1, e, i_max, B, x, N_trial[1])\n",
    "    #print(\"P1 done\")\n",
    "    p2 = descentDirection(g2, e, i_max, B, x, N_trial[2])\n",
    "    #print(\"P2 done\")\n",
    "    \n",
    "    while True:\n",
    "        alpha = alpha_start**degree\n",
    "        \n",
    "        first = check1(x, alpha, p0, N_trial[0], N_tilda)\n",
    "        second = check2(N_trial[0], N_tilda, alpha, p0)\n",
    "        third = check3(x, alpha, p0, N_trial[0], theta, N_start, N_tilda)\n",
    "        if first and second and third:\n",
    "            print(\"Alpha: %s | Trial value: %s\" %(alpha,0))\n",
    "            return alpha, p0, N_trial[0]\n",
    "            break\n",
    "        else:\n",
    "            first = check1(x, alpha, p1, N_trial[1], N_tilda)\n",
    "            second = check2(N_trial[1], N_tilda, alpha, p1) \n",
    "            third = check3(x, alpha, p1, N_trial[1], theta, N_start, N_tilda)\n",
    "            if first and second and third:\n",
    "                print(\"Alpha: %s | Trial value: %s\" %(alpha,1))\n",
    "                return alpha, p1, N_trial[1]\n",
    "                break\n",
    "            else:\n",
    "                first = check1(x, alpha, p2, N_trial[2], N_tilda)\n",
    "                second = check2(N_trial[2], N_tilda, alpha, p2) \n",
    "                third = check3(x, alpha, p2, N_trial[2], theta, N_start, N_tilda)\n",
    "                if first and second and third:\n",
    "                    print(\"Alpha: %s | Trial value: %s\" %(alpha,2))\n",
    "                    return alpha, p2, N_trial[2]\n",
    "                    break\n",
    "                else:\n",
    "                    if degree >= 10:\n",
    "                        print(\"forced to stop\")\n",
    "                        return alpha, p2, N_trial[2]\n",
    "                        break\n",
    "                    degree += 1\n",
    "                    print(degree)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def backtracking_normal(B, x, N_trial, N_tilda, N_start, theta):\n",
    "    alpha_start = 0.5\n",
    "    degree = 0\n",
    "    g = hl_der(x, N_trial)\n",
    "    p = descentDirection(g,e,i_max,B,x,N_trial)\n",
    "    while True:\n",
    "        alpha = alpha_start**degree\n",
    "        \n",
    "        first = check1(x, alpha, p, N_trial, N_tilda)\n",
    "        third = check3(x, alpha, p, N_trial, theta, N_start, N_tilda)\n",
    "        #print(first, third)\n",
    "        if first and third:\n",
    "            #print(\"Alpha: %s\" %alpha)\n",
    "            return alpha, p\n",
    "            break\n",
    "        else:\n",
    "            if degree >= 10:\n",
    "                #print(\"forced to stop\")\n",
    "                return alpha, p\n",
    "                break\n",
    "            degree += 1\n",
    "            #print(degree)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "N_max = train.shape[0]\n",
    "B0 = np.identity(x0.size)\n",
    "gama = math.pow(10,-4)\n",
    "e = math.pow(10,-4)\n",
    "m = math.pow(10,-4)\n",
    "theta0 = 0.9\n",
    "i_max = 100\n",
    "gama_k = 1\n",
    "r = 0.95\n",
    "l = math.pow(10,-5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def algorithm(method): \n",
    "    start = time()\n",
    "    \n",
    "    max_fev = 0\n",
    "    MAX_FEV = []\n",
    "    MAX_FEV.append(max_fev)\n",
    "    \n",
    "    f_Ntrain = []\n",
    "    N_tilda = []\n",
    "    trial_N = []\n",
    "    ITER = []\n",
    "    ACC = []\n",
    "    F1 = []\n",
    "    T = []\n",
    "    X = []\n",
    "    P = []\n",
    "    B = []\n",
    "    G = []\n",
    "    C = []\n",
    "    A = []\n",
    "    N = []\n",
    "    \n",
    "    X.append(x0)\n",
    "    if method == \"IRNS\":\n",
    "        N0 = math.ceil(0.1*N_max)\n",
    "        N.append(N0)\n",
    "        g0 = hl_der(x0, N[0])\n",
    "        G.append(g0)\n",
    "        max_fev += N[0]\n",
    "        MAX_FEV.append(max_fev)\n",
    "        f_Ntrain.append(hinge_loss(X[0],N_max))\n",
    "         \n",
    "    if method == \"HEUR\":\n",
    "        N0 = math.ceil(0.1*N_max)\n",
    "        N.append(N0)\n",
    "        g0 = hl_der(x0, N[0])\n",
    "        G.append(g0)\n",
    "        max_fev += N[0]\n",
    "        MAX_FEV.append(max_fev)\n",
    "        f_Ntrain.append(hinge_loss(X[0],N_max))\n",
    "        \n",
    "    if method == \"FSS\":\n",
    "        N0 = train.shape[0]\n",
    "        N.append(N0)\n",
    "        g0 = hl_der(X[0], N[0])\n",
    "        G.append(g0)\n",
    "        max_fev += N[0]\n",
    "        MAX_FEV.append(max_fev)\n",
    "        f_Ntrain.append(hinge_loss(X[0],N_max))\n",
    "        \n",
    "    N_tilda.append(0)\n",
    "    T.append(theta0)\n",
    "    B.append(B0)\n",
    "\n",
    "    \n",
    "    k = 0\n",
    "    ITER.append(0)\n",
    "    #while max_fev <= 10**5:\n",
    "    while k <= 100:\n",
    "        if method == \"IRNS\":\n",
    "            N_tilda.append(new_sample(N[k]))\n",
    "            T.append(find_theta(X[k], N_tilda[k+1], N[k], T[k]))\n",
    "            \n",
    "            #N_trial = round(0.8*N_tilda[-1])\n",
    "            if k == 0:\n",
    "                N_trial = first_N_trial(N[0], N_tilda[1], T[1], theta_k(T[1]), 1, X[0])\n",
    "            else:\n",
    "                N_trial = p_N_trial(N[k], N_tilda[k+1], T[k+1], theta_k(T[k+1]), A[-1], P[k-1] ,X[k])\n",
    "            \n",
    "            trial_N.append(N_trial)\n",
    "            three_canditates = candidates(N_trial, N_tilda[k+1])\n",
    "            C.append(three_canditates)\n",
    "            print(C[k])\n",
    "            alpha, p, N_new = backtracking_IRNS(B[k],X[k],three_canditates,N_tilda[k+1],N[k],T[k+1])\n",
    "            N.append(N_new)\n",
    "            max_fev += N[-1]\n",
    "            MAX_FEV.append(max_fev)\n",
    "            \n",
    "        if method == \"HEUR\":\n",
    "            N_tilda.append(new_sample(N[k]))\n",
    "            T.append(find_theta(X[k], N_tilda[k+1], N[k], T[k]))\n",
    "            N_trial = round(min(1.1*N[k],N_max))\n",
    "            N.append(N_trial)\n",
    "            alpha, p = backtracking_normal(B[k],X[k],N_trial,N_tilda[k+1],N[k],T[k+1])\n",
    "            max_fev += N[-1]\n",
    "            MAX_FEV.append(max_fev)\n",
    "            \n",
    "        if method == \"FSS\":\n",
    "            N_tilda.append(train.shape[0])\n",
    "            T.append(find_theta(X[k], N_tilda[k+1], N[k], T[k]))\n",
    "            N_trial = train.shape[0]\n",
    "            alpha, p = backtracking_normal(B[k], X[k], N_trial, N_tilda[k+1], N[k], T[k+1])\n",
    "            N.append(train.shape[0])\n",
    "            max_fev += N[-1]\n",
    "            MAX_FEV.append(max_fev)\n",
    "\n",
    "        P.append(p)\n",
    "        A.append(alpha)\n",
    "\n",
    "        #Step 4\n",
    "        sk = alpha*P[k]\n",
    "        X.append(X[k]+sk)\n",
    "        f_Ntrain.append(hinge_loss(X[-1],N_max))\n",
    "    \n",
    "\n",
    "        F, Accuracy = scores(k, X[-1])\n",
    "        F1.append(F)\n",
    "        ACC.append(Accuracy)\n",
    "\n",
    "        #print(\"Descent: %s | Semiposdef: %s \" %(np.matmul(G[-1],P[-1])<0,np.all(np.linalg.eigvals(B[-1]) > 0)))\n",
    "\n",
    "        #Step 5\n",
    "        G.append(hl_der(X[k+1], N[k+1]))\n",
    "        yk = G[k+1]-G[k]\n",
    "        if np.matmul(sk,yk) >= m*LA.norm(yk,2)**2:\n",
    "            B.append(BFGS(B[k], G[k+1], G[k], X[k+1], X[k]))\n",
    "        else:\n",
    "            B.append(B[k])\n",
    "\n",
    "        #Step 6\n",
    "        k += 1 \n",
    "        ITER.append(k)\n",
    "        print(\"-------------------------------------\")\n",
    "    end = time()\n",
    "    b = end - start\n",
    "    print(' ')\n",
    "    print('Time elapsed: %s minutes and %s seconds.'%(np.floor(b / 60), np.round(b % 60)))\n",
    "    return F1, ACC, MAX_FEV, f_Ntrain, N, ITER"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[369, 369, 369]\n",
      "1\n",
      "Alpha: 0.5 | Trial value: 0\n",
      "Iteration: 0 | Accuracy: 0.0 | F1: 0.469\n",
      "-------------------------------------\n",
      "[388, 433, 478]\n",
      "Alpha: 1.0 | Trial value: 0\n",
      "Iteration: 1 | Accuracy: 0.693 | F1: 0.531\n",
      "-------------------------------------\n",
      "[361, 429, 496]\n",
      "Alpha: 1.0 | Trial value: 0\n",
      "Iteration: 2 | Accuracy: 0.573 | F1: 0.643\n",
      "-------------------------------------\n",
      "[365, 418, 470]\n",
      "1\n",
      "Alpha: 0.5 | Trial value: 0\n",
      "Iteration: 3 | Accuracy: 0.723 | F1: 0.654\n",
      "-------------------------------------\n",
      "[370, 422, 474]\n",
      "Alpha: 1.0 | Trial value: 0\n",
      "Iteration: 4 | Accuracy: 0.757 | F1: 0.748\n",
      "-------------------------------------\n",
      "[376, 428, 479]\n",
      "Alpha: 1.0 | Trial value: 0\n",
      "Iteration: 5 | Accuracy: 0.79 | F1: 0.798\n",
      "-------------------------------------\n",
      "[383, 434, 485]\n",
      "Alpha: 1.0 | Trial value: 0\n",
      "Iteration: 6 | Accuracy: 0.816 | F1: 0.813\n",
      "-------------------------------------\n",
      "[389, 440, 491]\n",
      "Alpha: 1.0 | Trial value: 0\n",
      "Iteration: 7 | Accuracy: 0.835 | F1: 0.82\n",
      "-------------------------------------\n",
      "[396, 447, 497]\n",
      "Alpha: 1.0 | Trial value: 1\n",
      "Iteration: 8 | Accuracy: 0.839 | F1: 0.828\n",
      "-------------------------------------\n",
      "[453, 503, 552]\n",
      "Alpha: 1.0 | Trial value: 0\n",
      "Iteration: 9 | Accuracy: 0.827 | F1: 0.825\n",
      "-------------------------------------\n",
      "[459, 509, 558]\n",
      "Alpha: 1.0 | Trial value: 0\n",
      "Iteration: 10 | Accuracy: 0.845 | F1: 0.839\n",
      "-------------------------------------\n",
      "[465, 515, 564]\n",
      "Alpha: 1.0 | Trial value: 0\n",
      "Iteration: 11 | Accuracy: 0.834 | F1: 0.825\n",
      "-------------------------------------\n",
      "[470, 520, 569]\n",
      "Alpha: 1.0 | Trial value: 1\n",
      "Iteration: 12 | Accuracy: 0.836 | F1: 0.828\n",
      "-------------------------------------\n",
      "[524, 573, 621]\n",
      "1\n",
      "Alpha: 0.5 | Trial value: 0\n",
      "Iteration: 13 | Accuracy: 0.842 | F1: 0.835\n",
      "-------------------------------------\n",
      "[528, 577, 625]\n",
      "Alpha: 1.0 | Trial value: 1\n",
      "Iteration: 14 | Accuracy: 0.844 | F1: 0.836\n",
      "-------------------------------------\n",
      "[580, 628, 676]\n",
      "1\n",
      "Alpha: 0.5 | Trial value: 0\n",
      "Iteration: 15 | Accuracy: 0.846 | F1: 0.841\n",
      "-------------------------------------\n",
      "[584, 632, 679]\n",
      "Alpha: 1.0 | Trial value: 0\n",
      "Iteration: 16 | Accuracy: 0.853 | F1: 0.846\n",
      "-------------------------------------\n",
      "[588, 635, 682]\n",
      "Alpha: 1.0 | Trial value: 2\n",
      "Iteration: 17 | Accuracy: 0.85 | F1: 0.843\n",
      "-------------------------------------\n",
      "[685, 730, 775]\n",
      "Alpha: 1.0 | Trial value: 0\n",
      "Iteration: 18 | Accuracy: 0.84 | F1: 0.831\n",
      "-------------------------------------\n",
      "[688, 733, 778]\n",
      "Alpha: 1.0 | Trial value: 1\n",
      "Iteration: 19 | Accuracy: 0.849 | F1: 0.841\n",
      "-------------------------------------\n",
      "[736, 780, 824]\n",
      "Alpha: 1.0 | Trial value: 2\n",
      "Iteration: 20 | Accuracy: 0.855 | F1: 0.847\n",
      "-------------------------------------\n",
      "[827, 869, 910]\n",
      "1\n",
      "2\n",
      "Alpha: 0.25 | Trial value: 0\n",
      "Iteration: 21 | Accuracy: 0.854 | F1: 0.847\n",
      "-------------------------------------\n",
      "[829, 871, 913]\n",
      "Alpha: 1.0 | Trial value: 1\n",
      "Iteration: 22 | Accuracy: 0.851 | F1: 0.844\n",
      "-------------------------------------\n",
      "[872, 914, 955]\n",
      "Alpha: 1.0 | Trial value: 1\n",
      "Iteration: 23 | Accuracy: 0.857 | F1: 0.849\n",
      "-------------------------------------\n",
      "[916, 956, 996]\n",
      "Alpha: 1.0 | Trial value: 1\n",
      "Iteration: 24 | Accuracy: 0.864 | F1: 0.855\n",
      "-------------------------------------\n",
      "[959, 998, 1036]\n",
      "Alpha: 1.0 | Trial value: 0\n",
      "Iteration: 25 | Accuracy: 0.865 | F1: 0.857\n",
      "-------------------------------------\n",
      "[962, 1001, 1039]\n",
      "Alpha: 1.0 | Trial value: 2\n",
      "Iteration: 26 | Accuracy: 0.862 | F1: 0.854\n",
      "-------------------------------------\n",
      "[1040, 1078, 1115]\n",
      "1\n",
      "Alpha: 0.5 | Trial value: 1\n",
      "Iteration: 27 | Accuracy: 0.856 | F1: 0.847\n",
      "-------------------------------------\n",
      "[1079, 1116, 1152]\n",
      "Alpha: 1.0 | Trial value: 2\n",
      "Iteration: 28 | Accuracy: 0.86 | F1: 0.85\n",
      "-------------------------------------\n",
      "[1154, 1188, 1222]\n",
      "Alpha: 1.0 | Trial value: 1\n",
      "Iteration: 29 | Accuracy: 0.859 | F1: 0.847\n",
      "-------------------------------------\n",
      "[1189, 1223, 1256]\n",
      "Alpha: 1.0 | Trial value: 2\n",
      "Iteration: 30 | Accuracy: 0.857 | F1: 0.847\n",
      "-------------------------------------\n",
      "[1257, 1289, 1321]\n",
      "Alpha: 1.0 | Trial value: 2\n",
      "Iteration: 31 | Accuracy: 0.856 | F1: 0.849\n",
      "-------------------------------------\n",
      "[1322, 1352, 1382]\n",
      "Alpha: 1.0 | Trial value: 2\n",
      "Iteration: 32 | Accuracy: 0.857 | F1: 0.847\n",
      "-------------------------------------\n",
      "[1383, 1412, 1440]\n",
      "Alpha: 1.0 | Trial value: 1\n",
      "Iteration: 33 | Accuracy: 0.85 | F1: 0.841\n",
      "-------------------------------------\n",
      "[1413, 1441, 1469]\n",
      "Alpha: 1.0 | Trial value: 2\n",
      "Iteration: 34 | Accuracy: 0.855 | F1: 0.846\n",
      "-------------------------------------\n",
      "[1470, 1497, 1523]\n",
      "Alpha: 1.0 | Trial value: 2\n",
      "Iteration: 35 | Accuracy: 0.862 | F1: 0.854\n",
      "-------------------------------------\n",
      "[1525, 1550, 1574]\n",
      "Alpha: 1.0 | Trial value: 2\n",
      "Iteration: 36 | Accuracy: 0.86 | F1: 0.85\n",
      "-------------------------------------\n",
      "[1574, 1599, 1623]\n",
      "1\n",
      "2\n",
      "Alpha: 0.25 | Trial value: 2\n",
      "Iteration: 37 | Accuracy: 0.86 | F1: 0.849\n",
      "-------------------------------------\n",
      "[1624, 1647, 1669]\n",
      "Alpha: 1.0 | Trial value: 1\n",
      "Iteration: 38 | Accuracy: 0.863 | F1: 0.854\n",
      "-------------------------------------\n",
      "[1648, 1670, 1692]\n",
      "Alpha: 1.0 | Trial value: 2\n",
      "Iteration: 39 | Accuracy: 0.862 | F1: 0.854\n",
      "-------------------------------------\n",
      "[1693, 1714, 1735]\n",
      "Alpha: 1.0 | Trial value: 2\n",
      "Iteration: 40 | Accuracy: 0.857 | F1: 0.849\n",
      "-------------------------------------\n",
      "[1735, 1756, 1776]\n",
      "Alpha: 1.0 | Trial value: 2\n",
      "Iteration: 41 | Accuracy: 0.857 | F1: 0.849\n",
      "-------------------------------------\n",
      "[1776, 1796, 1815]\n",
      "1\n",
      "2\n",
      "Alpha: 0.25 | Trial value: 2\n",
      "Iteration: 42 | Accuracy: 0.86 | F1: 0.852\n",
      "-------------------------------------\n",
      "[1815, 1834, 1852]\n",
      "Alpha: 1.0 | Trial value: 2\n",
      "Iteration: 43 | Accuracy: 0.859 | F1: 0.852\n",
      "-------------------------------------\n",
      "[1852, 1870, 1887]\n",
      "1\n",
      "Alpha: 0.5 | Trial value: 2\n",
      "Iteration: 44 | Accuracy: 0.858 | F1: 0.85\n",
      "-------------------------------------\n",
      "[1887, 1904, 1920]\n",
      "Alpha: 1.0 | Trial value: 2\n",
      "Iteration: 45 | Accuracy: 0.861 | F1: 0.854\n",
      "-------------------------------------\n",
      "[1921, 1936, 1951]\n",
      "Alpha: 1.0 | Trial value: 2\n",
      "Iteration: 46 | Accuracy: 0.858 | F1: 0.85\n",
      "-------------------------------------\n",
      "[1951, 1966, 1981]\n",
      "Alpha: 1.0 | Trial value: 2\n",
      "Iteration: 47 | Accuracy: 0.861 | F1: 0.854\n",
      "-------------------------------------\n",
      "[1981, 1995, 2009]\n",
      "Alpha: 1.0 | Trial value: 2\n",
      "Iteration: 48 | Accuracy: 0.858 | F1: 0.85\n",
      "-------------------------------------\n",
      "[2009, 2023, 2036]\n",
      "Alpha: 1.0 | Trial value: 2\n",
      "Iteration: 49 | Accuracy: 0.859 | F1: 0.852\n",
      "-------------------------------------\n",
      "[2036, 2049, 2062]\n",
      "Alpha: 1.0 | Trial value: 2\n",
      "Iteration: 50 | Accuracy: 0.858 | F1: 0.85\n",
      "-------------------------------------\n",
      "[2062, 2074, 2086]\n",
      "Alpha: 1.0 | Trial value: 2\n",
      "Iteration: 51 | Accuracy: 0.858 | F1: 0.85\n",
      "-------------------------------------\n",
      "[2086, 2098, 2109]\n",
      "Alpha: 1.0 | Trial value: 2\n",
      "Iteration: 52 | Accuracy: 0.857 | F1: 0.849\n",
      "-------------------------------------\n",
      "[2109, 2120, 2131]\n",
      "Alpha: 1.0 | Trial value: 2\n",
      "Iteration: 53 | Accuracy: 0.858 | F1: 0.85\n",
      "-------------------------------------\n",
      "[2131, 2142, 2152]\n",
      "Alpha: 1.0 | Trial value: 2\n",
      "Iteration: 54 | Accuracy: 0.86 | F1: 0.852\n",
      "-------------------------------------\n",
      "[2152, 2162, 2172]\n",
      "1\n",
      "2\n",
      "Alpha: 0.25 | Trial value: 2\n",
      "Iteration: 55 | Accuracy: 0.86 | F1: 0.852\n",
      "-------------------------------------\n",
      "[2172, 2182, 2191]\n",
      "1\n",
      "Alpha: 0.5 | Trial value: 2\n",
      "Iteration: 56 | Accuracy: 0.858 | F1: 0.849\n",
      "-------------------------------------\n",
      "[2191, 2200, 2209]\n",
      "1\n",
      "Alpha: 0.5 | Trial value: 2\n",
      "Iteration: 57 | Accuracy: 0.857 | F1: 0.849\n",
      "-------------------------------------\n",
      "[2209, 2218, 2226]\n",
      "Alpha: 1.0 | Trial value: 2\n",
      "Iteration: 58 | Accuracy: 0.858 | F1: 0.849\n",
      "-------------------------------------\n",
      "[2226, 2234, 2242]\n",
      "Alpha: 1.0 | Trial value: 2\n",
      "Iteration: 59 | Accuracy: 0.854 | F1: 0.846\n",
      "-------------------------------------\n",
      "[2242, 2250, 2257]\n",
      "1\n",
      "Alpha: 0.5 | Trial value: 2\n",
      "Iteration: 60 | Accuracy: 0.857 | F1: 0.849\n",
      "-------------------------------------\n",
      "[2257, 2265, 2272]\n",
      "Alpha: 1.0 | Trial value: 2\n",
      "Iteration: 61 | Accuracy: 0.857 | F1: 0.849\n",
      "-------------------------------------\n",
      "[2272, 2279, 2286]\n",
      "Alpha: 1.0 | Trial value: 2\n",
      "Iteration: 62 | Accuracy: 0.858 | F1: 0.85\n",
      "-------------------------------------\n",
      "[2286, 2293, 2299]\n",
      "Alpha: 1.0 | Trial value: 2\n",
      "Iteration: 63 | Accuracy: 0.861 | F1: 0.852\n",
      "-------------------------------------\n",
      "[2299, 2306, 2312]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Alpha: 1.0 | Trial value: 2\n",
      "Iteration: 64 | Accuracy: 0.859 | F1: 0.85\n",
      "-------------------------------------\n",
      "[2312, 2318, 2324]\n",
      "Alpha: 1.0 | Trial value: 2\n",
      "Iteration: 65 | Accuracy: 0.858 | F1: 0.849\n",
      "-------------------------------------\n",
      "[2324, 2330, 2335]\n",
      "Alpha: 1.0 | Trial value: 2\n",
      "Iteration: 66 | Accuracy: 0.86 | F1: 0.852\n",
      "-------------------------------------\n",
      "[2335, 2341, 2346]\n",
      "Alpha: 1.0 | Trial value: 2\n",
      "Iteration: 67 | Accuracy: 0.86 | F1: 0.852\n",
      "-------------------------------------\n",
      "[2346, 2351, 2356]\n",
      "Alpha: 1.0 | Trial value: 2\n",
      "Iteration: 68 | Accuracy: 0.859 | F1: 0.85\n",
      "-------------------------------------\n",
      "[2356, 2361, 2366]\n",
      "Alpha: 1.0 | Trial value: 2\n",
      "Iteration: 69 | Accuracy: 0.858 | F1: 0.85\n",
      "-------------------------------------\n",
      "[2366, 2371, 2375]\n",
      "Alpha: 1.0 | Trial value: 2\n",
      "Iteration: 70 | Accuracy: 0.861 | F1: 0.854\n",
      "-------------------------------------\n",
      "[2375, 2380, 2384]\n",
      "Alpha: 1.0 | Trial value: 2\n",
      "Iteration: 71 | Accuracy: 0.86 | F1: 0.852\n",
      "-------------------------------------\n",
      "[2384, 2388, 2392]\n",
      "Alpha: 1.0 | Trial value: 1\n",
      "Iteration: 72 | Accuracy: 0.852 | F1: 0.844\n",
      "-------------------------------------\n",
      "[2388, 2392, 2396]\n",
      "Alpha: 1.0 | Trial value: 2\n",
      "Iteration: 73 | Accuracy: 0.855 | F1: 0.847\n",
      "-------------------------------------\n",
      "[2396, 2400, 2404]\n",
      "1\n",
      "2\n",
      "Alpha: 0.25 | Trial value: 2\n",
      "Iteration: 74 | Accuracy: 0.856 | F1: 0.849\n",
      "-------------------------------------\n",
      "[2404, 2408, 2411]\n",
      "1\n",
      "Alpha: 0.5 | Trial value: 2\n",
      "Iteration: 75 | Accuracy: 0.855 | F1: 0.847\n",
      "-------------------------------------\n",
      "[2411, 2415, 2418]\n",
      "Alpha: 1.0 | Trial value: 2\n",
      "Iteration: 76 | Accuracy: 0.855 | F1: 0.847\n",
      "-------------------------------------\n",
      "[2418, 2422, 2425]\n",
      "Alpha: 1.0 | Trial value: 2\n",
      "Iteration: 77 | Accuracy: 0.855 | F1: 0.847\n",
      "-------------------------------------\n",
      "[2425, 2428, 2431]\n",
      "Alpha: 1.0 | Trial value: 2\n",
      "Iteration: 78 | Accuracy: 0.855 | F1: 0.847\n",
      "-------------------------------------\n",
      "[2431, 2434, 2437]\n",
      "1\n",
      "Alpha: 0.5 | Trial value: 2\n",
      "Iteration: 79 | Accuracy: 0.857 | F1: 0.849\n",
      "-------------------------------------\n",
      "[2437, 2440, 2443]\n",
      "1\n",
      "Alpha: 0.5 | Trial value: 2\n",
      "Iteration: 80 | Accuracy: 0.857 | F1: 0.849\n",
      "-------------------------------------\n",
      "[2443, 2446, 2448]\n",
      "Alpha: 1.0 | Trial value: 2\n",
      "Iteration: 81 | Accuracy: 0.858 | F1: 0.85\n",
      "-------------------------------------\n",
      "[2448, 2451, 2453]\n",
      "Alpha: 1.0 | Trial value: 2\n",
      "Iteration: 82 | Accuracy: 0.861 | F1: 0.854\n",
      "-------------------------------------\n",
      "[2453, 2456, 2458]\n",
      "1\n",
      "Alpha: 0.5 | Trial value: 2\n",
      "Iteration: 83 | Accuracy: 0.861 | F1: 0.854\n",
      "-------------------------------------\n",
      "[2458, 2461, 2463]\n",
      "Alpha: 1.0 | Trial value: 2\n",
      "Iteration: 84 | Accuracy: 0.859 | F1: 0.85\n",
      "-------------------------------------\n",
      "[2463, 2465, 2467]\n",
      "Alpha: 1.0 | Trial value: 2\n",
      "Iteration: 85 | Accuracy: 0.859 | F1: 0.85\n",
      "-------------------------------------\n",
      "[2467, 2469, 2471]\n",
      "Alpha: 1.0 | Trial value: 2\n",
      "Iteration: 86 | Accuracy: 0.859 | F1: 0.85\n",
      "-------------------------------------\n",
      "[2471, 2473, 2475]\n",
      "Alpha: 1.0 | Trial value: 2\n",
      "Iteration: 87 | Accuracy: 0.861 | F1: 0.852\n",
      "-------------------------------------\n",
      "[2475, 2477, 2479]\n",
      "Alpha: 1.0 | Trial value: 2\n",
      "Iteration: 88 | Accuracy: 0.86 | F1: 0.852\n",
      "-------------------------------------\n",
      "[2479, 2481, 2483]\n",
      "Alpha: 1.0 | Trial value: 2\n",
      "Iteration: 89 | Accuracy: 0.86 | F1: 0.852\n",
      "-------------------------------------\n",
      "[2483, 2485, 2486]\n",
      "Alpha: 1.0 | Trial value: 2\n",
      "Iteration: 90 | Accuracy: 0.86 | F1: 0.852\n",
      "-------------------------------------\n",
      "[2486, 2488, 2489]\n",
      "Alpha: 1.0 | Trial value: 2\n",
      "Iteration: 91 | Accuracy: 0.86 | F1: 0.852\n",
      "-------------------------------------\n",
      "[2489, 2491, 2492]\n",
      "1\n",
      "Alpha: 0.5 | Trial value: 2\n",
      "Iteration: 92 | Accuracy: 0.861 | F1: 0.854\n",
      "-------------------------------------\n",
      "[2492, 2494, 2495]\n",
      "Alpha: 1.0 | Trial value: 2\n",
      "Iteration: 93 | Accuracy: 0.86 | F1: 0.852\n",
      "-------------------------------------\n",
      "[2495, 2497, 2498]\n",
      "1\n",
      "Alpha: 0.5 | Trial value: 2\n",
      "Iteration: 94 | Accuracy: 0.858 | F1: 0.85\n",
      "-------------------------------------\n",
      "[2498, 2500, 2501]\n",
      "1\n",
      "Alpha: 0.5 | Trial value: 2\n",
      "Iteration: 95 | Accuracy: 0.858 | F1: 0.85\n",
      "-------------------------------------\n",
      "[2501, 2502, 2503]\n",
      "1\n",
      "Alpha: 0.5 | Trial value: 2\n",
      "Iteration: 96 | Accuracy: 0.86 | F1: 0.852\n",
      "-------------------------------------\n",
      "[2503, 2504, 2505]\n",
      "Alpha: 1.0 | Trial value: 2\n",
      "Iteration: 97 | Accuracy: 0.86 | F1: 0.852\n",
      "-------------------------------------\n",
      "[2505, 2506, 2507]\n",
      "Alpha: 1.0 | Trial value: 2\n",
      "Iteration: 98 | Accuracy: 0.86 | F1: 0.852\n",
      "-------------------------------------\n",
      "[2507, 2508, 2509]\n",
      "Alpha: 1.0 | Trial value: 1\n",
      "Iteration: 99 | Accuracy: 0.86 | F1: 0.852\n",
      "-------------------------------------\n",
      "[2508, 2509, 2510]\n",
      "Alpha: 1.0 | Trial value: 2\n",
      "Iteration: 100 | Accuracy: 0.858 | F1: 0.85\n",
      "-------------------------------------\n",
      " \n",
      "Time elapsed: 10.0 minutes and 31.0 seconds.\n"
     ]
    }
   ],
   "source": [
    "F1_IRNS, ACC_IRNS, MAX_FEV_IRNS, f_Ntrain_IRNS, N_IRNS, ITER_IRNS = algorithm(\"IRNS\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "-------------------------------------\n",
      " \n",
      "Time elapsed: 4.0 minutes and 35.0 seconds.\n"
     ]
    }
   ],
   "source": [
    "F1_HEUR, ACC_HEUR, MAX_FEV_HEUR, f_Ntrain_HEUR, N_HEUR, ITER_HEUR = algorithm(\"HEUR\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      " \n",
      "Time elapsed: 5.0 minutes and 51.0 seconds.\n"
     ]
    }
   ],
   "source": [
    "F1_FSS, ACC_FSS, MAX_FEV_FSS, f_Ntrain_FSS, N_FSS, ITER_FSS = algorithm(\"FSS\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Graphics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 5))\n",
    "plt.plot(ACC_IRNS)\n",
    "plt.plot(ACC_HEUR)\n",
    "plt.plot(ACC_FSS)\n",
    "plt.legend([\"IRNS\", \"Heuristics\",\"Full sample size\"], loc =\"lower right\")\n",
    "plt.xlabel(\"Iteration\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.yscale(\"log\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 5))\n",
    "plt.plot(F1_IRNS)\n",
    "plt.plot(F1_HEUR)\n",
    "plt.plot(F1_FSS)\n",
    "plt.legend([\"IRNS\", \"Heuristics\",\"Full sample size\"], loc =\"lower right\")\n",
    "plt.xlabel(\"Iteration\")\n",
    "plt.ylabel(\"F1 Score\")\n",
    "plt.yscale(\"log\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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x2neew+BIiMtyWrTSJkNsprMem9m9nq6w1UuX11LR4ASskt2Bq/tR0t3SVde8d2urMXSHrXDGpkUza2wKabFhpMeFMyw2jPTYcJKjQwkMGESXtT/H4A5kPuj+++/nhhtu4KijjqKzs5OZM2fy8MMPc9FFF/H4448zZcoUpk+fzujRow94rLVr13LLLbcQEBBAcHAwf/nLXwC47rrrmDt3LmlpaSxcuLDn9RMmTOCnP/0pp5xyCoGBgUydOpX58+dzyy23sHXrVqy1zJ49m8mTJ/fb7y8iB+a1XqpbqilpLKG0sZTSptKeZVmjE7paOlv2+pnwoHDSItNIi0xjfOJ4hkUMIz0qnWGRw0iLTCM1IpXgQD+cQPqLaGvcE7LqdvZaL3TWG8v3+QHjtF7FZUHGMTD+PKeVa3fYis2EsDh1ju+ltaOLkroWSmpbKNkdtGpbKO5e3+VppbNXB3mAmLAg0uPCyYgL55jsuJ71tNhw0uOcy4iDoWXrSDHW2gO/ykdNmzbN7u6btdvGjRsZN26cSxXJkaRzKYOVtZbq1j2Bq6SxZE/46n7sO/xDXGgc6VHppEemkxaV5iwj03rWY0NjB1cH+d46WpxwVVcItTv2rNftdJb7dpQPDOkOVllO6Iod3r3M2tPqNVjD6SFqbu90AlZtC8W1zc6yztkuqW2hqnHvGzcCDKTFOgErPS6MjPhw0uPCe4WuMKLD9Bnvyxizwlo7ra/n1EImItIPGtsbKWksobihmOLGYoobivcKXvv234oPjScjKoPR8aOZlTXLCV9R6WREZZAWmebfcx8eSFen00+rdocTsmp3di93OOtNFXu/PjAE4oY7j7TJEJe9Zzs2yxnQdIiPsbWv1o4uimubKarpFbh6ha/qpr3/AxASGEBGvBOuTh+XQkZceM92Rnw4w2LCCFLr1hGlQCYicgi6vF2UN5dT1FC0V+javV7XVrfX66ODo8mIziA3NpeTMk4iIyqDzOhM0iOd4DWoAxc4o8XXFHSHrN3LHU7g8hTtPe6WCXQ6xcdlw+g5EJfjjL21O3gpcH1GZ5eXMk8rRbXNFNU4wauwprl7+7MtXCFBAWR2h6s56bFkxod3PyLIig8nKSqUgCHSd8tXKJCJiOxHW1cbxQ3FFNYXUtRQ5Dwai3pau3pP3xNkgnpatOZkzyEjOoPMqMyeZWxorIu/yQCw1umrVbO9+1HgBK/dy32HhAhPgIRcSJ8KEy5wpu+Jz3aWMZkac6sPnpYOCqubKazZ8yjqXpbWtezVhyswwJAeF0ZmXASnjU0mKz6CrIQIshKc0JWswOVz9CdeRIa0ls6WnsBV2FBIYX0hhQ3OdnlTOZY9X3JRwVFkRWcxOn40s4fPJis6i6zoLDKjM0mNSB18Y2/ty+t1Rpavzu8VvHqFr47mPa81gU4/roRcGH++s4zP3RO8wgZ5QD0EXq+lvKGVndXNFFY3s7OmyVmvaWZndTOelr3vUkyIDCErIYLJWXHMm5zWE7qGJ0SQFqtLiv5mkP/rISLitHQV1Rexs34nOxt2UlhfyM56Z1nRsnf/pISwBLKis5ieOp2smCyGRw/vCV5xoXGDt+P8brtbuqq3OcGrepsTuqrzndDV2avvW2CIE7ISRsCIU53QtTt4xQ1Xx/k+dHZ5Ka1rZUd1Ezurm9hR3czOaid47axppr1zz3iUQQGGjPhwhidEMG9yGsMTIhieEMnw7pYudZofXBTIRGRQ8FovZU1l7PTspKC+wAlf3Y/SxtK9WroSwhIYHj2c49KPY3j0cLJjshke4wSv6JBoF3+LAdRaD9Vb94Sunkc+tDfueV1giBO4EkbCqNO717u3Y9IhINC938FHdXktJbUtFHSHroKqJnZUOaGrqLaZjq49fxbDggPITogkNymSWWNTGJ4QQXZiBNkJkaTHqZVrKFEg6weBgYFMmjSpZ/vFF18kJyenz9funlbpwQcf/MwE425YtGgR9957b88gtYfq7LPP5sknnyQuLu7IFCbSrbG9kR31OyjwFFDgKehZL2oo2mtOxajgKLJjspmcPJnzRp5Hdkx2T/AaMqHL2+XcrVi1Daq2OAGsapuz7D02lwlwOswn5sHwEyBxpPNIGOlcdlTo+gxrLeX1bWyvauwJXAXdj6KaFtq79rR0RYQEkp0Yydi0aM6cOIzcxEgndCVGkhoTOvhbXeULUSDrB/vOZTkUvfrqq26XIH7MWkt5czkFngK2e7b3hK8CTwGVLXumsAk0gWRFZ5ETk8NJGSeRE5NDdkw2ObE5JIYlDp0vuvYmqNra/dgCVZud9ep86D3xd3gCJI2GUWc44StxFCSNci4xBoW4V78Pa2jtoKCqie2VTWyvbGR79/qO6iaa2/dM8xMaFEBOYiR5KVGcPj6VEUmR5CQ6LV/J0QpdcmAKZAMkJyeH5cuXk5SUxPLly7n55ptZtGjRF/rZZ599lp///OcEBgYSGxvL4sWL2bFjB1/72tdoamoC4MEHH+SEE05g0aJF3HHHHaSmprJq1SouvPBCJk2axH333UdLSwsvvvgiI0eO5OqrryYsLIz169dTXl7OH/7wB84555y93repqYnvfe97rF27ls7OTu68807OO++8vV5TVlbGpZdeSn19PZ2dnfzlL3/h5JNP7vl9//vf//Lwww8D4PF4yMnJYeHChbz55pvccccdtLW1MXLkSB577DGioqIO/4MWv+K1XkoaSsj35JNfl98TvrZ7ttPU0dTzuujgaHJjczkh/QRyYnPIjc0lNzaXrKiswTv6fF9a6qBysxO4Kns9PIV7XmMCnICVNBryTneWSaOd4BWR4FrpvqzLaymtayG/spH8yibyKxud8FXZREXDnkAbYCAzPoIRyZHMGJHAiOQochMjyU2OJC0mTHctymHxmUBmjDkV+AWwHnjaWrvIzXoOR0tLC1OmTAEgNzeXF1544bCOd9ddd/HGG2+QkZFBXV0dACkpKbz11luEhYWxdetWLr/8cnbPWrB69Wo2btxIQkICI0aM4Jvf/CaffPIJ9913Hw888AB/+tOfANixYwfvvfce+fn5zJo1i23btu31vvfccw+nnXYajz76KHV1dRx77LGcfvrpe012/uSTT3LmmWfy05/+lK6uLpqbm/c6xvXXX8/1119PR0cHp512Gj/4wQ+oqqri7rvv5u233yYyMpLf/OY3/OEPf/jcidHFv+0OXtvqtpHvyWdb3Ta21znhq/cAqSnhKeTG5XLuyHMZETvCecSNGFqtXeCM2VWxCSo2dIeujc6yoWzPa4LCnBaurOkw9QpIHuMEr8SREBTqXu0+rLWji+3dgWtbRWPPsqCqibZeneljw4MZmRzJzNHJjEiOZERSFCOTIxmeGEFokC7fSv/o10BmjHkUOAeosNZO7LX/LOA+IBD4u7X214AFGoEwoPhIvP9vPvkNm2o2HYlD9RibMJYfH/vjz33Nkb5keeKJJ3L11VdzySWXcOGFFwLQ0dHBd7/7XVatWkVgYCBbtmzpef306dNJS0sDYOTIkcyZMweASZMm7TW35SWXXEJAQACjRo1ixIgRbNq092f15ptv8tJLL3HvvfcC0NraSmFh4V7TGU2fPp1rrrmGjo4Ozj///J4guq8bb7yR0047jXnz5vHyyy+zYcMGTjzxRADa29s5/vjjD/NTEl9graWiuYKtdVvZVrvNWXaHr97Ba1jkMEbGjWT6sOmMjBvZE7xiQmJcrN4F7c1Oa1f5Bid8VWyEyk3OqPW7BUc4YWvEqZA81llPHuvcxai+XX1qbOtka3kDWysaya9wQte2ykYKa5rZPVugMZAVH8HI5EhOyksiLyWKEclO8EqIDBla/wEQn9DfLWTzgQeBx3fvMMYEAg8BZ+AEr2XGmJeA96217xljUoE/AF/t59oGVFBQEF6v8z+w1tbWA7x6bw8//DBLly7llVdeYcqUKaxatYoHHniA1NRUVq9ejdfrJSwsrOf1oaF7/nccEBDQsx0QEEBn556BLPf9B2ffbWstzz33HGPGjNlvbTNnzmTx4sW88sorfO1rX+OWW27hyiuv3Os18+fPZ+fOnTz44IM9xz3jjDN46qmnDupzEN/S2N7ItrptbKndwpbaLWytdcJXfXt9z2uSw5MZGTeSi0dfTF5cHnnxeYyMHUlUyBC7PO3tckalL1/nhK/ydU4AqymA3Xd/BoZC8mjIOckJXCnjIWWsMw+jRqXvU31rB1vLG3vC19aKRraVN1Dq2fNvbEhgACOSI5mYEcv5UzLIS4kiLyWK3KRIwoIVaMV39Gsgs9YuNsbk7LP7WGCbtXY7gDHmaeA8a+2G7udrgSPS3n6glqyBlJOTw4oVK5g7dy7PPffcQf1sfn4+M2bMYMaMGSxYsICioiI8Hg+ZmZkEBATwz3/+k66urgMfaB/PPvssV111FQUFBWzfvp0xY8awZMmSnufPPPNMHnjgAR544AGMMXz66adMnTp1r2Ps3LmTjIwMrr32Wpqamli5cuVegWzFihXce++9vP/++wR0f6kcd9xx3HDDDWzbto28vDyam5spLi5m9OjRB/07SP/zWi/FDcVsrt3M5prNPQGspHFPK05UcBSj4kdxZs6ZjIofRV5cHqPiRhEXFude4W5pqYPy9d2Ptc6yYuOeQVNNgHP3YupEOOpSSBnnhK/4XI1Ovx/N7Z1sLW9kc3kDW3Y1sLm8ga3ljeyq3xO8woIDyEuJYsaIRPJSohjVHbyGJ0Ro6AjxC2787c8AinptFwMzjDEXAmcCcTitan0yxlwHXAcwfPjw/qvyCLvjjjv4xje+wS9/+UtmzJhxUD97yy23sHXrVqy1zJ49m8mTJ/Od73yHiy66iGeffZZZs2bt1a/rixozZgynnHIK5eXlPPzww3u1sgHcfvvtfP/73+eoo47CWktOTs5nhsNYtGgRv/vd7wgODiYqKorHH398r+cffPBBampqmDVrFgDTpk3j73//O/Pnz+fyyy+nrc3pMHv33XcrkPmAls4WttVuY1PtJjbX7AlgzZ1OmAgwAeTE5DApaRIXjbqI0fGjGRU/irTItKF3icdaqCuEXWv3fvTuYB+eAMMmwtFXOcuU8U7rV8ggn7fyEHV0eSmoamLTrgY276pn865GNpfXU1TT0vOa0CAneB0/MpFRqVGMTolmdGo0mfHh6lQvfs1Yaw/8qsN5A6eF7OXdfciMMV8GzrTWfrN7+2vAsdba7x3ssadNm2Z3d2TfbePGjXv1cZK+XX311ZxzzjlcfPHFbpeyXzqX/cvT5mFTzSY21WxiY81GNlVvoqC+AK91Lq1HBUcxJmEMYxPGMiZ+DKMTRjMydiRhQWEHOPIg1NXpDCexaw2UrXGWu9Y4ne8BMM5djMMmQeoESJ3kBLDoNKezkuzFWktFQxsby+rZvKuBTd2P/IrGnvG7AgMMuUmRjEmNZswwJ3SNGRbN8IQIAhW8xE8ZY1ZYa6f19ZwbLWTFQFav7Uyg1IU6RIaM2tZaNlRvYGPNRmdZvZHixj33zqREpDAuYRynZ5/OuIRxjE0cS3pk+tBr9QLo6nA61peugrJVULYadq2Dzu5WmqAwJ3RNuNAJYGmTncuOIQffSj0UtHV2sbW8kY1l9Wza1dCzrGlq73nNsJgwxqZFM3N0EmOHRTMmNYaRKZG6o1GGFDcC2TJglDEmFygBLgO+4kIdQ9r8+fPdLkH6iafNw/rq9Wyo3sCG6g2sr1pPadOe//NkRmUyPnE8F42+iHEJ4xiXOI6EsCE6PpW3yxlOovTTPY9da/cMphoSDWlHwbRrnGXaZGeoCfX16lNtUzsbyurZUFrPhrJ6NpbVs62ikU6vcyUmLDiAManRzBmfythh0YxNi2HssGjiIjQorUh/D3vxFHAqkGSMKQbusNb+wxjzXeANnGEvHrXWrj/I484D5uXl5R3pkkX8SktnC5tqNrGuah1rq9ayvmo9hQ17+jBlRmUyKXkSl429jPGJ4xmbMJbY0FgXK3aRtc6djqUroaT7UbYadg9AGxIFaVPg2GshfaqznjBCdzj2wVpLSV0L60rquwOYh/Wl9ZT1urtxWEwY49KimT0uhXFpMYxLiyEnMVKXG0X2o7/vsrx8P/tfBQ55bh1r7QJgwbRp067dz/ND81LLINLffRv9kdd62eHZwZqqNaytXMvaqrVsqd1Cl3XusE2JSGFi4kTOzzufCUkTmJA4YeiGL4CWWihZAcUroGS5s95c7TwXGOq0eB39NSd8pR/tTCWk8PUZXq9lR3UT60rrWV/iYV13+Kpr7gCc0etHJEdxbG4C49NiGJ8ew/i0GBKjNDityMEYdO3uYWFhVFdXk5g4xEb2HkSstVRXV3/mrs+hxtPmYV3VOlZXrmZN5RrWVK2hob0BcDrcT0iawDUTr2Fi0kQmJU0iOSLZ5Ypd5O1y+n0VfQLFy5xH1e7Bko0zmOrouZBxNGQc49ztqLkbP2N3+Fpb4mFtsYe1JR42lNbT0OaMXxgSGMCYYdHMnTiM8emxTEiPYdywGMJD1NdL5HANukCWmZlJcXExlZWVB36x+KywsDAyMzPdLmPAWGspbCjk04pPWVWxilUVq8j35APOUBN5cXmcmXMmRyUdxeTkyeTE5hBghnBrTluDE7oKl0LRUiheDt1hlYhEyJzujPGVOc1pAQsbwi2F+2GtpaimhdXFdT0BbF2Jpyd8hQYFMC4thvOnZjAxI4aJGbGMSokmJGgI/7kT6Ud+Gcg+rw9ZcHAwubm5A1+UyEHo6OpgY81GPq34lJXlK1lVuYqa1hoAokOimZw8mbm5c5mcMplJSZOIDB7id/DVl8LOj6BwifOoWA/WC5juAVa/DJnHQtaxTr8vtY5/RmVDG6uL6lhdXMfqYg9ri+uo7b7sGBIYwLh0J3xNyoxlUkYso1KiNKCqyADq93HI+lNf45CJ+KKWzhbWVK5heflyVpSvYG3l2p65HbOis5iaMpWpKVOZkjyFEXEjhnbrl7VQnQ87P4CdH0PhR84ArADBkc5k2lnHOeErczqEDbH5L7+A5vZO1hZ7WF1cx6qiOlYXeSipc4btCDAwOjWayZlxHJUVy+TMOEanquVLZCD42jhkIoNec0czn1Z8yrJdy1hevpz11evp9HYSYAIYEz+Gi0df3BPChnTfL3ACWOVm2PE+7PzQaQlrLHeei0iC7ONhxrdh+HEw7CgNObEPr9dSUN3Ep4V1fFpYy6eFdWwub6Cre6iJzPhwpg6P4+sn5jA5K44J6TFEhOgzFPE1+lspcgS0dLb0BLBPdn3ChqoNdNpOgkwQE5ImcOX4K5mWOo0pKVOIDol2u1x3WQvV26BgsRPCdnwATd19PqPTIXcmZJ/oPJJG6fLjPhpaO1hd5GFlYS0ruwOYp8W59BgdGsTkrDi+c+pIpmTFMTkrjiTd7SjiFxTIRA5Bh7eDdVXrWFK2hE/KPmF15Wo6vB09AezqiVczfdh0piRPISJY8xbiKYGC92D7e86yoczZH50GI2ZB7smQc5IzwbYCWI/dHe9XFNawYmcty3fUsrm8AWudj2lUShRzJw5j6vA4jh4ez8jkKM3nKOKn/DKQaWBYGWjWWgrqC/i49GM+Lv2YZbuW0dzZjMEwNmEsXx33VWakzeDolKMVwMC5C3LHB5D/LuQvhOqtzv6IRKcFLHcm5J6iDvj76OjysrGsnmU7alm+o4blO2upbHBmDYgKDWLq8DjOnDCMY7LjmTI8jpiwYJcrFpEjRZ36RfbD0+ZhSdkSPir9iI9KP2JX0y7A6YR/fNrxHJd+HNNTpxMXFuduob7A63Xmfdz2Dmxf6AxF4e2E4Ajn0uOIU2DEqZAyQYOv9tLc3smnhXV8UlDDsh01fFpYR0uHM9BvZnw403MSODo7nmnZ8YxOjdYo9yJ+Tp36Rb4Ar/WyoXoDH5R8wIclH7Kmag1e6yU6OJrj0o/j2knXcnz68WRFZ7ldqm9oqnJawLa+Bfnv7BkFP20KnPA9GHkaZM2AIPVh2s3T0sHyHTUsLXAe60s8dHotAQbGpcVw6fQspuXEMy07gWGxQ3tgZJGhRoFMhrSG9gY+Lv2YxcWL+aDkA6pbqzEYJiRO4NpJ13JSxklMTJpIUID+qmAt7FoDW95wHiUrAOvcCZl3uvMYeRpEJrldqc+obWrvDl/VLN1ew8Zd9VjrjPs1OSuWb50yoqcVTJcfRYY2fcvIkFPUUMSiokUsKlrEyvKVdNpOYkJiODHjRE7OOJkTM04kISzB7TJ9Q0cLbF8Em1+DrW92d8Y3zhREp/4ERp0OaVN1GbJbXXM7S7bXsGR7NUu2V7NplzN7QFhwAMdkx3PT6aM5NjeBKVlxhAVruiER2UOBTAY9r/Wyrmodi4oWsbBoIdvqtgGQF5fHVROuYmbmTI5KPkqtYLs1VcGW12HTq84lyc4WCImGvNNg1Jkw6gyISnG7Sp/Q2NbJsoIaPsqv4qP8ajaUOS1gYcEBTM9JYN7kdI4bkcCkjDgNvCoin8svv4F0l6UcSIe3gxXlK3hn5zu8W/guFS0VBJpApqVO46LpF3FK1inqC9abpxg2vgwbX4LCj51piWIyYeoVMGYu5JysybiBts4uPi2s48NtVXy4rYrVxR66vJaQoACOGR7PD04fzXEjE5mcqQAmIgdHd1nKoNHR1cHHZR/z5o43WVS8CE+bh7DAME7MOJHZw2czM3MmsaGaZLpHdT5s+B9sXAClK519KeNh3DwY+yVnVPwhPiSF12vZtKuBD7ZV8sG2aj4pqKa1w0tggOGozFhOHJnECSMTOTo7XpcgReSAdJelDFq9Q9i7Re/S0N5AVHAUp2SdwhnDz+CEjBMIDwp3u0zfUVMAG16E9S9A2WpnX/rRcPqdMHYeJKnVuby+lfe3VvHB1ko+2FZFVWM7AHkpUVw2fTgn5iUxY0SCOuGLyBGlQCZ+x2u9rChfwSvbX+GtnW9R315PVHAUpw0/jTnZczg+/XhCAnV5rUd9Gax/Htb+d09LWMYxMOduGH8exA13tz6XtXZ0sWxHDYu3VPL+1qqejvhJUSGclJfESaOSOSkvScNQiEi/UiATv2CtZWPNRl7Z/gqv73idiuYKwoPCOW34aczNmasQtq+WOqc/2NpnoeB9wELaZDjjLhh/PsRnu1ygu3ZUNbFocwWLtlSyZLtzGTIkMIBpOfHcOncsM0clM3ZYtKYhEpEBo0AmPq28qZxXCl5hQf4CttVtIyggiJMyTuKWabdwStYpuhzZW1cnbHsbVj/pDFPR1e5MTXTKj2HSxc5E3UNUa0cXH2+v5r3NlSzcXMHO6mYAcpMiuWz6cGaOTuK4EYlEhOifRBFxh1/+66O7LAe31s5W3i58m5e2vcSSsiVYLFOSp3D7cbdzZs6Z6pi/r13rYPVTsOYZaKpw5oucdg0cdYnTP2yIdswvrWvh3U0VLNxUwYf5VbR2eAkLDuD4EYlcc2Iup45JJjsx0u0yRUQA3WUpPmRD9Qae3/o8r25/lYaOBjKiMpg3ch7zRsxjeMzQ7uf0Ga31sO6/sPJxKP0UAoJh9Jkw5SuQd8aQHKLC67WsKq7j3Y0VvL2xvKcvWFZCOKeNSWHW2BSOG5GouyFFxDW6y1J8VmN7Iy9vf5nntz7PxpqNhASEcEbOGVyYdyHThk0jwGgspx7WQtEnTghb/zx0NDuTdZ/1G5j0ZYhMdLvCAdfc3sniLVW8s7GchZsrqGpsJzDAMC07ntvOHstpY1MYmRyFGaKthCLiPxTIxBWbajbxn83/4ZXtr9DS2cLYhLHcNuM2zs49W5ck99XW4FyOXPYPqFgPIVFOADv6KmcKoyEWNiob2nhnYzlvbSjng21VtHV6iQ4L4tQxKZw+LoVTRicTFzH0WghFxL8pkMmA6ejq4PUdr/P05qdZU7mG0MBQzso5i0vHXMrEpIlqxdhXxUYnhK1+GtobYNgkmHcfTLwYQqPcrm5A7axu4o31u3hjfTkrC2uxFjLiwrn82OHMGZ/K9NwEggPVmioi/kuBTPpddUs1z2x5hmc2P0NVSxU5MTn8aPqPOHfkuWoN25fXC9vego8fgoL3IDAUJlwA078JmdOGTGuYtZaNZQ28vn4Xb67f1dMfbHxaDDfOHsWc8cMYlxatEC8ig4YCmfSbzTWbeWLDE7xa8Cod3g5OyjiJK8ZdwfHpx6tv2L7am2DVk7D0YajeBtHpMPsO57LkEOkbZq1ldbGH19aV8fq6XeysbibAwLScBG4/ZzxzxqeSlRDhdpkiIv1CgUyOKGsty3Yt49F1j/Jh6YeEB4Vz4agL+eq4r5Ibm+t2eb6nsdIJYcv+Dq11zjAVF/3DGUE/cPBPzeP1Wj4tquPVtU4IK6lrISjAcPzIRL41cyRzJqSSFBXqdpkiIv3OLwOZxiHzPV3eLt4ufJvH1j3G+ur1JIYlcuPRN/Ll0V/WZcm+1O6Ejx6AT5+AzjZnMu8T/h9kHTvoL0v2DmGvrS2j1NNKSGAAJ49K4qYzRnPGuFRiIwZ/GBUR6U3jkMlh6fR28mrBqzyy5hF21u8kOyabqyZcxbkjzyU0UC0bn1G5BRb/DtY9ByYAJl8KJ9wIyaPdrqxfWWtZW+Lh5TVlvLy6tCeEzRydzJeOGsbscamarFtEBj2NQyZHXIe3g5fzX+Zva/9GUUMRYxPG8vtTfs/s4bMJDNDAm59RuQUW/9aZ4Ds4Ao77Nhz3HYjNcLuyfrV5VwMvrS7h5TVl7KxuJjjQMHNUMrecNUYhTESkFwUyOSid3k4W5C/gr2v+SkljCeMSxnHfrPuYlTVLd7z1Zd8gduKNcML3IDLJ7cr6TVFNMy+tLuWlVaVsLm8gMMBwwshEbjg1jzMnDNPlSBGRPiiQyRdireWdwne4/9P7KfAUMCFxAj859ifMzJypINYXTwks+qVz52RQ+KAPYtWNbbyytowXPy1hZWEdAMdkx3PXeRM4e1KaOuaLiByAApkc0Cdln/CnlX9ibdVacmNz+eOpf2T28NkKYn1pqYUP/ghL/wrWCzO+DSf/YFAGsZb2Lt7eWM6Ln5bw3pZKOr2WscOi+dFZY5h3VLqGqBAROQgKZLJf2+u287vlv+ODkg9IjUjlrhPuYt7IeQQF6I/NZ3S2OcNXvP97Z+LvyZfBrNsgbnBNiu71WpYW1PD8ymJeW7eLxrZOhsWE8Y2Tczl/Sgbj0mLcLlFExC/pm1U+w9Pm4S+r/8LTm54mIiiCHx7zQy4fd7numuyLtbD5NXjjNqgtgLwz4PQ7YdhEtys7ovIrG3lhZQkvfFpCSV0LUaFBzJ04jAuOzmBGbiKBAWotFRE5HApk0qPT28mzW57loVUP0dDewEWjLuK7U79LQliC26X5psrN8PqtkP8uJI2BK56HvNluV3XENLR28PKaMp5dXsTKwjoCDJw8KpkfnTWGOeOHER6iu2lFRI4UBTIBYHXlau76+C621G5hxrAZ3DL9FsYkjHG7LN/U1gALfwWf/BWCI+HMX8Gx1w6KkfWttSzZXsOzK4p4dW0ZrR1e8lKi+MncsZw/NYPUmDC3SxQRGZT8MpBppP4jp769nvtX3s8zm58hJSJFHfYPZPNr8MoPob4Ujr4SZv/foOiwX1HfyrMrinlmeRE7q5uJCg3igqmZXDItkylZcfrzICLSzzRS/xBlreWNHW/wm2W/oaa1hq+M/QrfnfpdIoMj3S7NNzXsgtd+BBv+BynjYd59zjRHfqyzy8t7Wyp5elkR726qoMtrOTY3gUunZXH2pDRdkhQROcI0Ur/spbK5kjs/vpPFxYsZnzieh2Y/xPjE8W6X5ZushRXz4a07oLMVTrvdmXMyKMTtyg5ZSV0L/1lWxDPLithV30pSVCjXnjyCS6ZlMiI5yu3yRESGJAWyIeb1gte5e+ndtHa2csu0W/jquK9qqqP9qS+D/90A+e9AzslOq1jiSLerOiSdXV4Wbq7kqU8KWbS5AgvMHJXMnedOYPa4FIIDA9wuUURkSFMgGyLqWuu4Z+k9vL7jdSYlTeLuk+5mROwIt8vyXetfhJe/Dx2tcPa9MP2b4If9qHZ5WvnPsiKeXlZImaeVlOhQbpiVxyXTsjRwq4iID1EgGwI+LPmQn334M+pa6/je1O9xzcRrNLjr/rR64LUfw+qnIP1ouPARSBrldlUHxeu1fJhfxb+W7OTtjU7fsJmjk7lj3gROH5dCkFrDRER8jr6VB7FObyd/XvVn/rb2b+TF5fHn2X9mXOI4t8vyXcXL4dmvQ30JnHIrzLzZr4ayqGtu578rivnXkp3sqG4mITKEb56cy1eOHU52om7WEBHxZQpkg1RlcyU/Wvwjlpcv58JRF3LrsbcSHhTudlm+yVpn2qM3b4eYNPjGm5DZ500wPmlNcR1PfLyTl1aX0tbpZVp2PN8/fTRzJw0jNEj9A0VE/IEC2SC0pGwJP178Y1o6W7jnpHs4d+S5bpfku1rq4KXvwsYFMOZLcP5DEB7vdlUH1NHl5dW1Zcz/aAefFtYRERLIRcdkcsWMbManaz5JERF/o0A2iFhr+ce6f3D/yvvJjc3l0TMfZWScf94VOCBKV8GzV4GnGObcA8ff4PMd96sa23hqaSH/WrqT8vo2chIj+L9zxnPxtExiwvzn8qqIiOxNgWyQaO9q586P7mTB9gXMzZ3LncffSUSw7qLrk7Ww4jGn835kMlz9Kgyf4XZVn2t9qYfHPtzBS6tKae/yMnN0Mr++MIdTRicToIm9RUT8ngLZIFDbWsv3F36flRUruWHKDXzrqG9pqpv9aW+Cl2+CNf+BkbPhwr9BZKLbVfWpy2t5e2M5j31YwJLtNYQHB3Lp9CyuOiGHvBQN4CoiMpgokPm57XXbueGdG6horuC3M3/L3Ny5bpfkuyq3wDNXQuUmmPVTOPlmCPC9ISAa2zp5ZlkR8z/aQWFNMxlx4dx29lgunTac2AhdlhQRGYwUyPzY0rKl3LTwJoIDg3n0rEeZnDzZ7ZJ817rn4H/fg+Bw+NoLMHKW2xV9RnFtM/M/3MF/lhXR0NbJMdnx3Dp3LHPGp2rsMBGRQU6BzE8tLl7MTQtvYnjMcB6a/RDpUelul+SbOtvhrdudYS2yjoMvPwYxvvVZrdhZy6MfFPDaujKMMZw9KY1vnJTLlKw4t0sTEZEB4peBzBgzD5iXl5fndimueGPHG9y6+FbGJIzh4dMfJi4szu2SfJOnBJ69Goo/geNugDN+7jMDvXZ2eXljfTl//2A7nxbWERMWxLUzR3DV8Tmkx2m8OBGRocZYa92u4ZBNmzbNLl++3O0yBtRL+S9x+4e3Mzl5Mg/NfojokGi3S/JN2xfBf78Bna1w3oMw4QK3KwKgobWD/ywr4rEPd1BS10J2YgTXnJjLxcdkEhnql/8/EhGRL8gYs8Ja2+fI4/oG8CPPbH6GXyz5BTOGzeD+0+7XsBZ98Xrhwz/Cu3dD4ii49AlIHuN2VVTUt/Lohzv499KdNLR2cmxuAnfMG8/scakEatgKEZEhT4HMTzy96WnuWXoPMzNn8vtTfk9YUJjbJfmeVg+88G3Y/ApMuBDOfQBC3R0eYltFA39bXMALn5bQ6fUyd2Ia180cwWT1DxMRkV4UyPzA6zte55dLf8mpmafyh1P/QLCP9IPyKRUb4T9XQE0BnPVrmHG9a6PuW2tZtqOWRxbn8/bGCsKCA7h0ehbfPDlXk3yLiEifFMh83NKypdz2/m1MTZnK7075ncJYX9Y9D//7LoREwlULIOdEV8ro8lre2rCLh9/bzqqiOuIjgrlx9iiuPD6bxKhQV2oSERH/oEDmwzZWb+TGhTeSHZPN/afdr8uU++rqhLfvgI8fhKwZ8OV/QkzagJfR1tnF8ytLeGTxdgqqmhieEMFd503gy8dkER4SOOD1iIiI/1Eg81FFDUV8++1vExMSw8OnP0xsaKzbJfmWpir479ehYDFMvxbO/CUEhQxoCY1tnTy5dCd/f7+AioY2JmbE8NBXjuasicPUUV9ERA6KApkPqm6p5vq3rqfTdvLoGY+SGpnqdkm+pXSV01+ssQLO/wtM+cqAvn11Yxv//GgH//x4J56WDk4YmcgfLpnCiXmJmkNUREQOiQKZj+n0dvLD935IRXMFfz/z74yIHeF2Sb5l9dOw4EaITIZvvAHpUwfsrQurm/nb+9t5ZnkR7V1e5oxP5dun5mlEfREROWwKZD7mgU8fYEX5Cn518q80N2VvXR3w5s+cKZByToYvz4fIpAF563UlHh5+L59X15YRGGC4YGoG180cSV6Ku0NqiIjI4KFA5kMWFi7k0XWPcsnoSzhnxDlul+M7mqrh2atgx/vdUyDdBYH9/0d3dVEd97+zlXc2VRAVGsS1J4/gmpNySY3RzRUiInJkKZD5iOKGYn764U8ZlzCOHx37I7fL8R271sLTX4GGcrjgrzD5sn5/y5WFtdz/zlYWba4kNjyYH54xmitPyCE2XEOOiIhI/1Ag8wFtXW38YNEPAPjDqX8gNFBjVgHd44vdAGFxcM1rkHFMv77dysJa/vT2VhZvqSQ+IpgfnTWGK4/PIUpzTIqISD/TN40P+O0nv2VjzUbun3U/mdGZbpfjPq8XFt4N7//eGV/skicguv/uNF1dVMcf397Cos2VJEaG8JO5Y7niuGxN9i0iIgNG3zgue33H6zyz5RmumXgNs4bPcrsc97U1wPPXweZX4egr4ex7Iah/WgzXlXj409tbeHtjBXERwfz4rLFcebyCmIiIDDx987jI0+bhV0t/xaSkSXxv6vfcLsd9tTvgqcuhcjPM/R0ce22/zEe5pbyBP761hdfW7SImLIib54zmqhNyiA5THzEREXGHApmL/rjij3jaPDxyxiMEBQzxU7HjA/jP18B2wRXPwcgj31q4s7qJP729lRdXlRAZEsSNs0fxjZNziVEQExERlw3xFOCeleUreW7rc3x9wtcZkzDG7XLctWI+vPJDSBgBlz8NiSOP6OF3eVq5752tPLu8iKBAw3Unj+Bbp4wkIXJgp1oSERHZHwUyF3R0dXDXx3eRHpnO9ZOvd7sc93i74M3bYclDkHc6XPwohB25OTvrmtv5y6J85n+0A6+1fHXGcG6YlUeKxhETEREfo0DmgsfWP0a+J5+HZj9ERHCE2+W4o60B/vsN2PoGzPg2zLn7iA322tTWyWMfFvDXxdtpbOvkgqkZ3HT6aLIShuhnLSIiPs+nApkxJhJYDNxhrX3Z7Xr6Q2F9IX9d/VfOyD6DmZkz3S7HHXWF8ORlULkJvvR7mP7NI3LYLq/luRXF/O7NzVQ2tHH6uFRuOXMMY4ZFH5Hji4iI9Jd+DWTGmEeBc4AKa+3EXvvPAu4DAoG/W2t/3f3Uj4Fn+rMmN1lr+cWSXxAcGMytx97qdjnuKF7u3EnZ2QZX/BdGnnZEDrtkezW/eHkD60vrmTo8joevOJpjshOOyLFFRET6W3+3kM0HHgQe373DGBMIPAScARQDy4wxLwHpwAZg0HbweWPnGywpW8JtM24jJSLF7XIG3voX4IXrIXoYXP0yJB/+zQyF1c386rWNvLZuF+mxYdx/+VTmHZWG6YfhMkRERPpLvwYya+1iY0zOPruPBbZZa7cDGGOeBs4DooBIYDzQYox51Vrr3feYxpjrgOsAhg8f3o/VH1le6+XhVQ+TF5fHJaMvcbucgWUtfHgfvH2HM/L+ZU9BZOJhHbK2qZ0HF27j8Y93EBQQwA/PGM03Tx5BeEjgESpaRERk4HxuIDPGPAg8aa396Ai+ZwZQ1Gu7GJhhrf1u93teDVT1FcYArLWPAI8ATJs2zR7BuvrVO4XvkO/J57czf0tgwBAKDV0d8OrNztAWEy6E8/8CwYfeCNra0cX8j3bw0MJtNLV18uVjsvjBnNGk6s5JERHxYwdqIdsK/N4Ykwb8B3jKWrvqMN+zr2tJPcHKWjv/MI/vc6y1PLLmEbJjspmTPcftcgZOaz08exXkvwsn/QBOux0CAg7pUF6v5cVVJdz7xmZKPa3MGpPMrXPHqcO+iIgMCp8byKy19wH3GWOygcuAx4wxYcBTwNPW2i2H8J7FQFav7Uyg9BCO4zfeL3mfTTWb+MWJvxg6rWP1pfDvL0PFRph3Pxxz1SEf6qNtVdzz6kbWl9YzMSOGe788mRPyko5gsSIiIu76Qn3IrLU7gd8AvzHGTAUeBe7AuUvyYC0DRhljcoESnKD3lYM5gDFmHjAvLy/vEN5+YFlr+evqv5Iemc6XRnzJ7XIGRsVG+NdF0OqBrz4LebMP6TBbyxv41WubeHdTBRlx4dx32RTmHZVOQIA67IuIyODyhQKZMSYYOAsnPM0G3gN+/gV+7ingVCDJGFOMM77YP4wx3wXewAl0j1pr1x9M0dbaBcCCadOmXXswP+eGpbuWsqZqDbcfdzvBAUNgzsQdH8DTX4GgMPj6a5B21EEforapnXvf3MxTnxQSGRrET+aO5aoTcggLHiKtiyIiMuQcqFP/GcDlwJeAT4CngeustU1f5ODW2sv3s/9V4NWDK9U/PbLmEVLCUzgv7zy3S+l/656HF74F8bnOGGNxB3cXbJfX8tQnhdz75mYaWju58vgc/t/sUZpzUkREBr0DtZDdBjwJ3GytrRmAegaVleUrWbZrGT+a/iNCA0PdLqd/ffxneOMnMPwEuOzfEHFwg7J+WljL//1vPWtLPMzITeCu8yaqw76IiAwZBwpkd1tr3wEwxuRaawt2P2GMudBa+3y/Vrcf/tKH7JE1j5AQlsBFoy5yu5T+Yy28fSd8+CcYfx5c8MhBDWuxy9PK79/czLMrikmNCdXAriIiMiQdKJD9Dji6e/25XusAPwNcCWT+0IdsfdV6Piz9kBuPvnHwTiDe1Qkv3wif/gumfQPO/h18wbtIG9s6eeS9fB55fzteL3xr5gi+N3sUUaE+Nb2qiIjIgDjQt5/Zz3pf29LLc1ufIzwonMvGXOZ2Kf2jowX+ew1sfhVOuRVOvRW+QKtWZ5eX/ywv4o9vbaWqsY15k9P50ZljyEoYpKFVRETkCzhQILP7We9rW7p1eDt4a+dbnJp1KlEhUW6Xc+S11DkThBd+DGffC8d+sYbKj/OrueOldWwpb+TYnAT+ftU0pmTF9WupIiIi/uBAgWxE98Tfptc63du5/VqZH1tatpS6tjrm5sx1u5Qjr7ESnrgAKjfBxY/CxAsP+CNlnhZ++eomFqwuJTM+nIevOIYzJ6Sqn5iIiEi3AwWy3mM13LvPc/tuDxhf79T/WsFrRAdHc2LGiW6XcmR5iuHx88BTAl/5zwEHfG3v9PKPDwp44N2tdHkt3z99FNefMlLjiYmIiOzjQIGswFpbOCCVHARf7tTf1tXGu4Xvcnr26YQEDqLxs6rznTDW6oGvvQDZx3/uy1fsrOHHz61lW0UjZ4xP5f/OGa9+YiIiIvtxoED2It13VhpjnrPWDuLxG46MD0o+oLGjcXBdrizfAE+cD95OuGoBpE/Z70ub2jr53Rub+efHO0iPDeexq6cza2zKgJUqIiLijw7mLssR/VnIYPF6wevEh8ZzbNqxbpdyZJSscOal3D0VUvKY/b500eYKfvrCOko9LVx1fA43nzlGw1iIiIh8AYdzl6Xso7mjmfeK3+PckecSFDAIgkjRJ04YC4+Hq16C+Jw+X1bT1M7dL2/g+U9LyEuJ4r/Xn8Ax2fEDW6uIiIgfO1BqmGyMqcdpKQvvXqd721prY/q1Oj/zXvF7tHS2cFbOWW6Xcvh2fgT//jJEpTqXKWMzPvMSay0vrS7l5ws20NDawf87LY8bTssjNEid9kVERA7G5wYya61PfrP66l2WrxW8Rkp4CkenHn3gF/uygvfhyUsgJsMJYzFpn3lJcW0zP3txHYs2VzIlK47fXHSU5p4UERE5RH55Xc0X77Ksb6/ng5IPuGzsZQSYALfLOXTbF8GTl0F8Nlz5EkSn7vW0tZYnluzk169tAuCOeeO58vgcAgM0ppiIiMih8stA5oveLXyXDm+Hf99due0dePorkDASrvwfRCXv9XR5fSs3P7ua97dWMXN0Mr+8YCKZ8RrKQkRE5HApkB0hrxe8TkZUBhOTJrpdyqHZvsgJY4mjnDAWmbjX06+uLeO2F9bS2tHFL86bwBXHZWukfRERkSNEgewIqGmtYUnZEr4+8ev+GVJ2fOjMTZkw4jNhrKG1gztf2sBzK4s5KjOWP146hZHJg3B+ThERERcpkB0B7xW9R5ft4sycM90u5eAVfeJ04I/N/EwYW7K9mh8+s5oyTwvfOy2P/zd7FMGBftw/TkRExEf5ZSDztbss11atJTo4mjHx+x801SftHvR199AWUc6I+q0dXdz7xmb+8WEB2QkRPKtxxURERPqVXwYyX7vLcn31esYnjvevy5Vlq+GJC7oHfV0A0cMAWFfi4ab/rGJrRSNXHDec284eR0SIX/4xERER8Rv6pj1M7V3tbKndwtfGf83tUr64qm3wxIUQEt0z6KvXa3l4cT5/eHMLCZEhzP/6dE4dozkoRUREBoIC2WHaWreVTm8n4xPHu13KF+MpdiYKB6fPWHw21Y1t3PTMahZvqeRLk9K454KJxEWEuFqmiIjIUKJAdpg2VG8AYELiBJcr+QKaqp3LlK0euPplSMpj2Y4avvfkp9Q0t3P3+RP56ozh/nXpVUREZBBQIDtM66vWExMSQ2ZUptulfL62Bvj3RVBXCFc8jzf1KB5etI3fv7mFrPhwnv/2CUzMiHW7ShERkSFJgewwbaje4Psd+jtanUFfy9bAZf+mMW0G339iOW9vrOCco9L41YWTiA4LdrtKERGRIUuB7DC0d7WztW4rV46/0u1S9s/rhRe+BQWL4YJHKEo+hW/++SO2VTby83MncOXxGnFfRETEbX4ZyHxlHLKttU6Hfp/uP/bW7bDhRZhzN0uiT+fbD36A18Lj1xzLiXlJblcnIiIigF8Ou26tXWCtvS421t0+T+ur1wP47h2Wn/wNPn4Qjr2OpwLP5Yq/LyU+MoQXbzhRYUxERMSH+GULma/YUL2B2NBYMqIy3C7lsza/Bq/9CDt6Lr/yXskjL6xj5uhkHrh8KrHh6i8mIiLiSxTIDsOG6g2MT/DBDv0lK+G/12CHTea2gO/z1AeFXHl8Nv93zniCNBeliIiIz9G38yFq62pja+1WJiT5WP+x2p3w5KV4I5L4YfBtPLWqmptOH83Pz52gMCYiIuKj1EJ2iLbWbqXT+tgI/W2N8NRl2M42bg6/mxe2dnDXeRO48vgctysTERGRz6FAdojWVzkd+n3mDsvu4S1s5SZ+FnUXL5VG86dLJ3PeFB/s3yYiIiJ7USA7RBtqNhAXGkdaZJrbpTgW/w42vcxDIdfwXF0ef7/qGE0OLiIi4icUyA7R+qr1TEic4Bsd+je+DIt+ycvmVP7Wfhb//uZ0jslOcLsqERER+YLUy/sQtHa2kl+X7xv9x8o30PXcdawhj98GXc+z3z5BYUxERMTP+GUgM8bMM8Y84vF4XHn/LbVb6LQ+MEJ/cw3Nj19CdUcIv4i8jaduOJXRqdHu1iQiIiIHzS8Dmdsj9W+o3gC4PEK/18uu+VcS2FjG7+J+xl+/M4+MuHD36hEREZFD5peBzG3rq9eTEJbAsMhhrtWw4bm7GVbxPv+Ou547vnM1CZEhrtUiIiIih0eB7BBsqN7AuMRxrnXoX7LoFUav+yMfh57EZd+5k6hQ3ZshIiLizxTIDtLuDv1u9R97b9Umhi/8HlWBKUz6zhNEhGpeShEREX+nppWDtLl2M122y5X+Y+9trqDz+e+QHOCh7WuvERWruylFREQGA7WQHaTC+kIARsaOHND3XbK9mo/+dRezA1bQMfvnROUeO6DvLyIiIv1HLWQHyWu9AAQFDNxHl1/ZyP2PP83jgU/SNupsIk66YcDeW0RERPqfApmPq2lq59uPfcDfeACihxF64Z/BF2YHEBERkSNGgcyHtXV2cf0TK7i8cT7ZAWVwwUsQHu92WSIiInKEqQ+Zj7LW8pPn18LOD7k64HWYfi2MOMXtskRERKQfqIXMRz20cBuvr8zno7hHMeHZcMbP3S5JRERE+okCmQ96fV0Z9765hX+lvURsbSlc/iqERLpdloiIiPQTXbL0McW1zdzy3zV8LXUHJ9W+iDnuO5B9gttliYiISD9SIPMhnV1evv/0KiJsM3d4H4LEPJh9u9tliYiISD/zy0BmjJlnjHnE4/G4XcoR9eDCbSzfWctTI94kqLEMzv8LBIe7XZaIiIj0M78MZNbaBdba62JjY90u5YhZtqOG+9/ZyvfGNTFix9Mw/ZuQpdH4RUREhgK/DGSDjae5g+8/vYqs+HC+3/43CE+AWbe5XZaIiIgMEAUyl1lrue3FtZTXt/LEtAICSz6B0+/UALAiIiJDiIa9cNnzK0t4ZU0Zt5+ewfAVN0LGNJjyVbfLEhERkQGkQOYiT0sHv3x1I0cPj+OajqehqRK+8h8IUMOliIjIUKJvfhf96e0t1DS385uTgzGfPALHXAUZR7tdloiIiAwwtZC5ZPOuBh7/eCdfmZ7FqOU/gbAYOO3/3C5LREREXKAWMhdYa7njpXVEhwXxk+xNsPMDOO12iEx0uzQRERFxgVrIXPDK2jKWbK/h7nPHEvXRJZAyHo652u2yRERExCUKZAOsub2Te17ZyPi0GL4S8QlUb4NLnoCAQLdLExEREZfokuUAe2jhNso8rdw1bwwB7/0Ghk2Csee4XZaIiIi4SC1kA2hndRN/W1zABVMzmOZ5E2oL4LKnNMyFiIjIEKckMIDuf2cbgQGGW+eMhPd+A2lTYMxct8sSERERlymQDZBdnlZeWl3CJdMySd3+HNQVwqyfgjFulyYiIiIuUyAbII99WECX1/LN4zNg8b2QOR1GneF2WSIiIuIDFMgGQENrB08uLWTupDSydj4HniKYdZtax0RERARQp/4B8dQnhTS0dXL9Cenw3Fdh+PEwYpbbZYmIiIiPUCDrZ+2dXh79YAfHjUhgUuXL0FAKF/5VrWMiIiLSQ5cs+9mC1aXsqm/lWyePgE/+BulTIXem22WJiIiID1Eg60fWWv72/nZGp0ZxavhWqNwE07/pdlkiIiLiY3wmkBljxhljHjbG/NcY82236zkS3ttSyaZdDVx78gjMsn9AWCxMuNDtskRERMTH9GsgM8Y8aoypMMas22f/WcaYzcaYbcaYWwGstRuttdcDlwDT+rOugfLI4u2kxoRyXl4QbFwAU66AkAi3yxIREREf098tZPOBs3rvMMYEAg8Bc4HxwOXGmPHdz50LfAC808919bt1JR4+yq/m6hNyCVnzL/B2wLRr3C5LREREfFC/BjJr7WKgZp/dxwLbrLXbrbXtwNPAed2vf8laewLw1f6sayD8b1UJIYEBfGV6BiyfDyNOhaQ8t8sSERERH+TGsBcZQFGv7WJghjHmVOBCIBR4dX8/bIy5DrgOYPjw4f1W5OFauLmSGSMSiC16F+qLYe6v3S5JREREfJQbgayvAbistXYRsOhAP2ytfQR4BGDatGn2iFZ2hBTVNLOtopHLpmfBst9AdDqM1iTiIiIi0jc37rIsBrJ6bWcCpS7U0W8WbakE4Iy0Zsh/B465GgI1Bq+IiIj0zY1AtgwYZYzJNcaEAJcBLx3MAYwx84wxj3g8nn4p8HAt2lTB8IQIhuc/DSYQjr7S7ZJERETEh/X3sBdPAR8DY4wxxcaYb1hrO4HvAm8AG4FnrLXrD+a41toF1trrYmNjj3zRh6m1o4sP86s4Y1QMZtW/YNw5EJPmdlkiIiLiw/r1Opq19vL97H+Vz+m478+WFtTQ2uHlovCV0FIL077hdkkiIiLi43xmpP7BYtHmCkKDAhhTu8jpzK95K0VEROQAFMiOsEWbK5k5IprA7QthzFwwfd1UKiIiIrKHXwYyX+3UX1DVREFVE5cmFkBHE4w52+2SRERExA/4ZSDz1U79izZXADCjfQmEREHuyS5XJCIiIv7ALwOZr1q0uZKRieFE73wb8mZDUKjbJYmIiIgfUCA7Qlrau/h4ezWXZ1VD4y4Y8yW3SxIRERE/4ZeBzBf7kH28vYr2Ti9zAlc6g8GOOsPtkkRERMRP+GUgc7MPmaXv6TMXbqokPDiQzIqFkH0CRCQMcGUiIiLir/wykPkC02s4C2stCzdXcF52GwGVG53hLkRERES+IAWyIyC/soni2hYujlrr7FAgExERkYOgQHYEfLy9GoCJjR9B8jhIGOFyRSIiIuJPFMiOgPyKRtJDWggtWaLWMRERETlofhnIfO0uy/zKRi6K2YixXTBWw12IiIjIwfHLQOZrI/XnVzQy2yyHqFRIP9rtckRERMTP+GUg8yXN7Z1UeRoY3/wJjD4LAvSRioiIyMFRejhM2yubOC5gAyFdzZpMXERERA6JAtlhyq9s5JiArVgToMnERURE5JAokB2m/Momxpud2MQ8CIl0uxwRERHxQ34ZyHzpLsv8ykYmBRUSMOwot0sRERERP+WXgcyX7rKsKN/FMFsJwya5XYqIiIj4Kb8MZL7C67WE12xwNhTIRERE5BApkB2GkroWRnm3OxsKZCIiInKIFMgOQ35lI+MDdtIengJRKW6XIyIiIn5Kgeww7L7DEnXoFxERkcOgQHYYCsuryQsoJThjstuliIiIiB8LcrsAf9ZWtoFguiBN/cdERETk0PllC5mvjEMWUbvRWdElSxERETkMfhnIfGEcssa2Toa3baMjIBzic12rQ0RERPyfXwYyX1BY3cz4gJ00xo+FAH2MIiIicuiUJA7RzqpGxplCAtLVoV9EREQOjzr1H6K6XQVEmxY6s6e6XYqIiIj4ObWQHSJvhdOhPyhNHfpFRETk8CiQHaJIzza6CICUcW6XIiIiIn5OgewQpbTtpCY8B4LD3S5FRERE/JwC2SHKM8U0J6h1TERERA6fOvUfohRTh02f4nYZIiIiMgiohewwxI04xu0SREREZBDwy0DmK1MnRQ2f4ur7i4iIyODgl4HMF6ZOqjUJEJno2vuLiIjI4OGXgcwXVEdo/koRERE5MhTIDlJTcwsAbfGjXK5EREREBgsFsoPkKd8JQFDqeJcrERERkcFCgewgtdWVARCVPtrlSkRERGSwUCA7WNYLQGBwqMuFiIiIyGChQCYiIiLiMgUyEREREZcpkImIiIi4TIFMRERExGUKZCIiIiIuUyATERERcZkCmYiIiIjL/DKQGWPmGWMe8Xg8bpciIiIictj8MpBZaxdYa6+LjY11uxQRERGRw+aXgUxERERkMFEgExEREXGZsda6XcMhM8ZUAjv7+W2SgKp+fg/54nQ+fIfOhW/R+fAdOhe+xZfOR7a1NrmvJ/w6kA0EY8xya+00t+sQh86H79C58C06H75D58K3+Mv50CVLEREREZcpkImIiIi4TIHswB5xuwDZi86H79C58C06H75D58K3+MX5UB8yEREREZephUxERETEZQpkn8MYc5YxZrMxZpsx5la36xlKjDFZxpiFxpiNxpj1xpgbu/cnGGPeMsZs7V7Gu13rUGGMCTTGfGqMebl7W+fCJcaYOGPMf40xm7r/jhyv8+EeY8xN3f9OrTPGPGWMCdP5GBjGmEeNMRXGmHW99u33szfG/KT7O32zMeZMd6rumwLZfhhjAoGHgLnAeOByY8x4d6saUjqBH1prxwHHATd0f/63Au9Ya0cB73Rvy8C4EdjYa1vnwj33Aa9ba8cCk3HOi86HC4wxGcD/A6ZZaycCgcBl6HwMlPnAWfvs6/Oz7/4OuQyY0P0zf+7+rvcJCmT7dyywzVq73VrbDjwNnOdyTUOGtbbMWruye70B5wsnA+cc/LP7Zf8EznelwCHGGJMJfAn4e6/dOhcuMMbEADOBfwBYa9uttXXofLgpCAg3xgQBEUApOh8Dwlq7GKjZZ/f+PvvzgKettW3W2gJgG853vU9QINu/DKCo13Zx9z4ZYMaYHGAqsBRItdaWgRPagBQXSxtK/gT8CPD22qdz4Y4RQCXwWPcl5L8bYyLR+XCFtbYEuBcoBMoAj7X2TXQ+3LS/z96nv9cVyPbP9LFPt6QOMGNMFPAc8H1rbb3b9QxFxphzgApr7Qq3axHAaY05GviLtXYq0IQuh7mmu3/SeUAukA5EGmOucLcq2Q+f/l5XINu/YiCr13YmTjO0DBBjTDBOGPu3tfb57t3lxpi07ufTgAq36htCTgTONcbswLl0f5ox5l/oXLilGCi21i7t3v4vTkDT+XDH6UCBtbbSWtsBPA+cgM6Hm/b32fv097oC2f4tA0YZY3KNMSE4HQFfcrmmIcMYY3D6yGy01v6h11MvAVd1r18F/G+gaxtqrLU/sdZmWmtzcP4evGutvQKdC1dYa3cBRcaYMd27ZgMb0PlwSyFwnDEmovvfrdk4fV51Ptyzv8/+JeAyY0yoMSYXGAV84kJ9fdLAsJ/DGHM2Tt+ZQOBRa+097lY0dBhjTgLeB9ayp9/SbTj9yJ4BhuP8Q/hla+2+HTqlnxhjTgVuttaeY4xJROfCFcaYKTg3WIQA24Gv4/wHW+fDBcaYnwOX4twd/inwTSAKnY9+Z4x5CjgVSALKgTuAF9nPZ2+M+SlwDc65+r619rWBr7pvCmQiIiIiLtMlSxERERGXKZCJiIiIuEyBTERERMRlCmQiIiIiLlMgExEREXGZApmI+D1jTGP3MscY85UjfOzb9tn+6EgeX0QEFMhEZHDJAQ4qkBljAg/wkr0CmbX2hIOsSUTkgBTIRGQw+TVwsjFmlTHmJmNMoDHmd8aYZcaYNcaYb4EzwK0xZqEx5kmcwYcxxrxojFlhjFlvjLmue9+vgfDu4/27e9/u1jjTfex1xpi1xphLex17kTHmv8aYTcaYf3eP4C4isl9BbhcgInIE3Ur3TAIA3cHKY62dbowJBT40xrzZ/dpjgYnW2oLu7WustTXGmHBgmTHmOWvtrcaY71prp/TxXhcCU4DJOKOELzPGLO5+biowAWeevA9x5gP94Ej/siIyeKiFTEQGsznAlcaYVTjTbiXizF8H8EmvMAbw/4wxq4ElOBMQj+LznQQ8Za3tstaWA+8B03sdu9ha6wVW4VxKFRHZL7WQichgZoDvWWvf2GunMydn0z7bpwPHW2ubjTGLgLAvcOz9aeu13oX+rRWRA1ALmYgMJg1AdK/tN4BvG2OCAYwxo40xkX38XCxQ2x3GxgLH9XquY/fP72MxcGl3P7VkYCbwyRH5LURkyNH/2kRkMFkDdHZfepwP3IdzuXBld8f6SuD8Pn7udeB6Y8waYDPOZcvdHgHWGGNWWmu/2mv/C8DxwGrAAj+y1u7qDnQiIgfFWGvdrkFERERkSNMlSxERERGXKZCJiIiIuEyBTERERMRlCmQiIiIiLlMgExEREXGZApmIiIiIyxTIRERERFymQCYiIiLisv8PUno6kGhJmW8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 5))\n",
    "plt.plot(MAX_FEV_IRNS)\n",
    "plt.plot(MAX_FEV_HEUR)\n",
    "plt.plot(MAX_FEV_FSS)\n",
    "plt.legend([\"IRNS\", \"Heuristics\",\"Full sample size\"], loc =\"upper left\")\n",
    "plt.xlabel(\"Iteration\")\n",
    "plt.ylabel(\"FEV\")\n",
    "plt.yscale(\"log\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 5))\n",
    "plt.plot(f_Ntrain_IRNS)\n",
    "plt.plot(f_Ntrain_HEUR)\n",
    "plt.plot(f_Ntrain_FSS)\n",
    "plt.legend([\"IRNS\", \"Heuristics\",\"Full sample size\"], loc =\"upper right\")\n",
    "plt.xlabel(\"Iteration\")\n",
    "plt.ylabel(\"f_Ntrain\")\n",
    "plt.yscale(\"log\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 5))\n",
    "plt.plot(MAX_FEV_IRNS[:-1],f_Ntrain_IRNS)\n",
    "plt.plot(MAX_FEV_HEUR[:-1],f_Ntrain_HEUR)\n",
    "plt.plot(MAX_FEV_FSS[:-1],f_Ntrain_FSS)\n",
    "plt.legend([\"IRNS\", \"Heuristics\",\"Full sample size\"], loc =\"upper right\")\n",
    "plt.xlabel(\"Mushrooms: FEV\")\n",
    "plt.ylabel(\"f_Ntrain\")\n",
    "plt.yscale(\"log\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 5))\n",
    "plt.plot(N_IRNS)\n",
    "plt.plot(N_HEUR)\n",
    "plt.plot(N_FSS)\n",
    "plt.legend([\"IRNS\", \"Heuristics\",\"Full sample size\"], loc =\"lower right\")\n",
    "plt.xlabel(\"Iteration\")\n",
    "plt.ylabel(\"Sample size\")\n",
    "plt.yscale(\"log\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}