prob_simulation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import keras\n",
    "import keras.layers as layers\n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "#import datetime as dt\n",
    "import numpy as np\n",
    "#import seaborn as sns\n",
    "import scipy.interpolate\n",
    "from scipy.ndimage import gaussian_filter1d\n",
    "\n",
    "def logNorm(inputs, scale, bias, mu, sigma):\n",
    "    X = np.maximum(inputs - bias, 0) + 1e-8\n",
    "    return scale * (np.exp(-(np.log(X) - mu)**2 / (2 * sigma**2)) / (X * sigma * np.sqrt(2 * np.pi)))\n",
    "\n",
    "def prefixSum(sigmaInc, id):\n",
    "    return sum(np.maximum(sigmaInc[i], 0) + 0.05 for i in range(0,id+1))\n",
    "    \n",
    "cols = ['Unnamed', 'Days', 'Catches']\n",
    "\n",
    "class ProbLayer(tf.keras.layers.Layer):\n",
    "  def __init__(self, scale, bias, mu, sigmaInc, **kwargs):\n",
    "    super(ProbLayer, self).__init__( **kwargs)\n",
    "    self.iscale = tf.math.log(scale)\n",
    "    self.ibias = bias\n",
    "    self.imu = mu\n",
    "    self.isigmaInc = sigmaInc\n",
    "\n",
    "  def build(self, input_shape):\n",
    "    self.total = len(self.ibias)\n",
    "    self.scale = self.add_weight(\"scale\", shape=(self.total,), \n",
    "               initializer=keras.initializers.Constant(self.iscale))\n",
    "    self.bias = self.add_weight(\"bias\", shape=(self.total,), \n",
    "               initializer=keras.initializers.Constant(self.ibias))\n",
    "    self.mu = self.add_weight(\"mu\", initializer=keras.initializers.Constant(self.imu))\n",
    "    #self.mu = self.add_weight(\"mu\", shape=(self.total,), initializer=keras.initializers.Constant(self.isigma))\n",
    "    self.sigmaInc = self.add_weight(\"sigmaInc\", shape=(self.total,), initializer=keras.initializers.Constant(self.isigmaInc))\n",
    "\n",
    "  def logNorm(self, inputs, scale, id):\n",
    "    # sigmas must be positive and always increase\n",
    "    sigma = sum(tf.nn.relu(self.sigmaInc[i]) + 0.01 for i in range(0,id+1))\n",
    "    X = tf.nn.relu(inputs - self.bias[id]) + 1e-9\n",
    "    return scale[id] * (tf.math.exp(-(tf.math.log(X) - self.mu)**2 / (2 * sigma**2)) / (X * sigma * tf.math.sqrt(2 * np.pi)))\n",
    "\n",
    "  def call(self, inputs):\n",
    "    scale = tf.nn.softmax(self.scale) # make sure scales some up to 1\n",
    "    A = sum(self.logNorm(inputs,scale,id) for id in range(0,self.total))\n",
    "    return A\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19009\n",
      "Scale =  [0.5, 0.4, 0.1]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "csv = pd.read_csv('C:/work/ImageProcDocs/Python-playground/FishPopulation/nosed.csv')\n",
    "y0 = csv[cols[2]]\n",
    "y = y0 / sum(y0)\n",
    "print(sum(y0))\n",
    "\n",
    "x0 = list(range(1,len(y)+1,1))\n",
    "fI = scipy.interpolate.interp1d(x0, y, kind='linear')\n",
    "xI = np.linspace(x0[0],x0[-1], 512)\n",
    "yI = fI(xI)\n",
    "zI = gaussian_filter1d(yI, 6, mode='nearest')\n",
    "\n",
    "scale = [0.5,0.4,0.1]\n",
    "print(\"Scale = \", scale)\n",
    "aa=11\n",
    "bias = [0 - aa,27-aa,50-aa]\n",
    "mu = 2.85\n",
    "sigma = [0.4,0.15,0.05]\n",
    "\n",
    "#bias=[-24.354303, 5.7943015, 26.09499] \n",
    "#mu = 3.3170514\n",
    "#sigma=[0.20614398,  0.09253789, -0.00944026]\n",
    "#print(list(prefixSum(sigma,i) for i in range(0,3)))\n",
    "\n",
    "zA = sum(logNorm(xI,scale[i],bias[i],mu,prefixSum(sigma,i)) for i in range(0,3))\n",
    "plt.plot(xI, yI)\n",
    "plt.plot(xI,zA)\n",
    "plt.plot(xI, zI)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_3\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " prob_layer_3 (ProbLayer)    (None, 1)                 10        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 10\n",
      "Trainable params: 10\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Epoch 1/3000\n",
      "8/8 [==============================] - 1s 11ms/step - loss: 5.8419e-05\n",
      "Epoch 2/3000\n",
      "8/8 [==============================] - 0s 10ms/step - loss: 5.4390e-05\n",
      "Epoch 3/3000\n",
      "8/8 [==============================] - 0s 10ms/step - loss: 5.0348e-05\n",
      "Epoch 4/3000\n",
      "8/8 [==============================] - 0s 12ms/step - loss: 4.6023e-05\n",
      "Epoch 5/3000\n",
      "8/8 [==============================] - 0s 10ms/step - loss: 4.1673e-05\n",
      "Epoch 6/3000\n",
      "8/8 [==============================] - 0s 10ms/step - loss: 3.7656e-05\n",
      "Epoch 7/3000\n",
      "8/8 [==============================] - 0s 10ms/step - loss: 3.3478e-05\n",
      "Epoch 8/3000\n",
      "8/8 [==============================] - 0s 11ms/step - loss: 2.9736e-05\n",
      "Epoch 9/3000\n",
      "8/8 [==============================] - 0s 10ms/step - loss: 2.5970e-05\n",
      "Epoch 10/3000\n",
      "8/8 [==============================] - 0s 10ms/step - loss: 2.2766e-05\n",
      "Epoch 11/3000\n",
      "8/8 [==============================] - 0s 10ms/step - loss: 1.9943e-05\n",
      "Epoch 12/3000\n",
      "8/8 [==============================] - 0s 10ms/step - loss: 1.7537e-05\n",
      "Epoch 13/3000\n",
      "8/8 [==============================] - 0s 10ms/step - loss: 1.5653e-05\n",
      "Epoch 14/3000\n",
      "8/8 [==============================] - 0s 11ms/step - loss: 1.4135e-05\n",
      "Epoch 15/3000\n",
      "8/8 [==============================] - 0s 10ms/step - loss: 1.2934e-05\n",
      "Epoch 16/3000\n",
      "8/8 [==============================] - 0s 9ms/step - loss: 1.1966e-05\n",
      "Epoch 17/3000\n",
      "8/8 [==============================] - 0s 10ms/step - loss: 1.1192e-05\n",
      "Epoch 18/3000\n",
      "8/8 [==============================] - 0s 10ms/step - loss: 1.0566e-05\n",
      "Epoch 19/3000\n",
      "8/8 [==============================] - 0s 10ms/step - loss: 1.0059e-05\n",
      "Epoch 20/3000\n",
      "8/8 [==============================] - 0s 10ms/step - loss: 9.6080e-06\n",
      "Epoch 21/3000\n",
      "8/8 [==============================] - 0s 10ms/step - loss: 9.2200e-06\n",
      "Epoch 22/3000\n",
      "8/8 [==============================] - 0s 10ms/step - loss: 8.9105e-06\n",
      "Epoch 23/3000\n",
      "8/8 [==============================] - 0s 10ms/step - loss: 8.6286e-06\n",
      "Epoch 24/3000\n",
      "8/8 [==============================] - 0s 10ms/step - loss: 8.3445e-06\n",
      "Epoch 25/3000\n",
      "8/8 [==============================] - 0s 10ms/step - loss: 8.0937e-06\n",
      "Epoch 26/3000\n",
      "8/8 [==============================] - 0s 10ms/step - loss: 7.8787e-06\n",
      "Epoch 27/3000\n",
      "8/8 [==============================] - 0s 11ms/step - loss: 7.6780e-06\n",
      "Epoch 28/3000\n",
      "8/8 [==============================] - 0s 10ms/step - loss: 7.5005e-06\n",
      "Epoch 29/3000\n",
      "8/8 [==============================] - 0s 11ms/step - loss: 7.3300e-06\n",
      "Epoch 30/3000\n",
      "8/8 [==============================] - 0s 12ms/step - loss: 7.1539e-06\n",
      "Epoch 31/3000\n",
      "8/8 [==============================] - 0s 13ms/step - loss: 7.0189e-06\n",
      "Epoch 32/3000\n",
      "8/8 [==============================] - 0s 11ms/step - loss: 6.8984e-06\n",
      "Epoch 33/3000\n",
      "8/8 [==============================] - 0s 11ms/step - loss: 6.7504e-06\n",
      "Epoch 34/3000\n",
      "8/8 [==============================] - 0s 11ms/step - loss: 6.6434e-06\n",
      "Epoch 35/3000\n",
      "8/8 [==============================] - 0s 9ms/step - loss: 6.5488e-06\n",
      "Epoch 36/3000\n",
      "8/8 [==============================] - 0s 10ms/step - loss: 6.4368e-06\n",
      "Epoch 37/3000\n",
      "8/8 [==============================] - 0s 10ms/step - loss: 6.3308e-06\n",
      "Epoch 38/3000\n",
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      "Epoch 2894/3000\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 2.6808e-06\n",
      "Epoch 2895/3000\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 2.6454e-06\n",
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      "8/8 [==============================] - 0s 8ms/step - loss: 2.6790e-06\n",
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      "Epoch 2898/3000\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 2.6821e-06\n",
      "Epoch 2899/3000\n",
      "8/8 [==============================] - 0s 9ms/step - loss: 2.6506e-06\n",
      "Epoch 2900/3000\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 2.6818e-06\n",
      "Epoch 2901/3000\n",
      "8/8 [==============================] - 0s 9ms/step - loss: 2.6598e-06\n",
      "Epoch 2902/3000\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 2.6803e-06\n",
      "Epoch 2903/3000\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 2.6706e-06\n",
      "Epoch 2904/3000\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 2.6632e-06\n",
      "Epoch 2905/3000\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 2.6553e-06\n",
      "Epoch 2906/3000\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 2.6674e-06\n",
      "Epoch 2907/3000\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 2.6564e-06\n",
      "Epoch 2908/3000\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 2.6622e-06\n",
      "Epoch 2909/3000\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 2.7033e-06\n",
      "Epoch 2910/3000\n",
      "8/8 [==============================] - 0s 9ms/step - loss: 2.6453e-06\n",
      "Epoch 2911/3000\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 2.6606e-06\n",
      "Epoch 2912/3000\n",
      "8/8 [==============================] - 0s 6ms/step - loss: 2.6568e-06\n",
      "Epoch 2913/3000\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 2.6507e-06\n",
      "Epoch 2914/3000\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 2.6479e-06\n",
      "Epoch 2915/3000\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 2.6497e-06\n",
      "Epoch 2916/3000\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 2.6455e-06\n",
      "Epoch 2917/3000\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 2.7007e-06\n",
      "Epoch 2918/3000\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 2.6835e-06\n",
      "Epoch 2919/3000\n",
      "8/8 [==============================] - 0s 6ms/step - loss: 2.6405e-06\n",
      "Epoch 2920/3000\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 2.6636e-06\n",
      "Epoch 2921/3000\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 2.6560e-06\n",
      "Epoch 2922/3000\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 2.6604e-06\n",
      "Epoch 2923/3000\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 2.6495e-06\n",
      "Epoch 2924/3000\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 2.6903e-06\n",
      "Epoch 2925/3000\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 2.6504e-06\n",
      "Epoch 2926/3000\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 2.6441e-06\n",
      "Epoch 2927/3000\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 2.6490e-06\n",
      "Epoch 2928/3000\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 2.6712e-06\n",
      "Epoch 2929/3000\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 2.6552e-06\n",
      "Epoch 2930/3000\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 2.6480e-06\n",
      "Epoch 2931/3000\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 2.6530e-06\n",
      "Epoch 2932/3000\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 2.6461e-06\n",
      "Epoch 2933/3000\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 2.6592e-06\n",
      "Epoch 2934/3000\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 2.6594e-06\n",
      "Epoch 2935/3000\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 2.6467e-06\n",
      "Epoch 2936/3000\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 2.6500e-06\n",
      "Epoch 2937/3000\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 2.6643e-06\n",
      "Epoch 2938/3000\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 2.6497e-06\n",
      "Epoch 2939/3000\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 2.6713e-06\n",
      "Epoch 2940/3000\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 2.6668e-06\n",
      "Epoch 2941/3000\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 2.6450e-06\n",
      "Epoch 2942/3000\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 2.6677e-06\n",
      "Epoch 2943/3000\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 2.6481e-06\n",
      "Epoch 2944/3000\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 2.6486e-06\n",
      "Epoch 2945/3000\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 2.6422e-06\n",
      "Epoch 2946/3000\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 2.6648e-06\n",
      "Epoch 2947/3000\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 2.6519e-06\n",
      "Epoch 2948/3000\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 2.6446e-06\n",
      "Epoch 2949/3000\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 2.6392e-06\n",
      "Epoch 2950/3000\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 2.6414e-06\n",
      "Epoch 2951/3000\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 2.6500e-06\n",
      "Epoch 2952/3000\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 2.6561e-06\n",
      "Epoch 2953/3000\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 2.6554e-06\n",
      "Epoch 2954/3000\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 2.6393e-06\n",
      "Epoch 2955/3000\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 2.6608e-06\n",
      "Epoch 2956/3000\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 2.6595e-06\n",
      "Epoch 2957/3000\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 2.6532e-06\n",
      "Epoch 2958/3000\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 2.6466e-06\n",
      "Epoch 2959/3000\n",
      "8/8 [==============================] - 0s 9ms/step - loss: 2.6512e-06\n",
      "Epoch 2960/3000\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 2.6505e-06\n",
      "Epoch 2961/3000\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 2.6973e-06\n",
      "Epoch 2962/3000\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 2.6393e-06\n",
      "Epoch 2963/3000\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 2.7004e-06\n",
      "Epoch 2964/3000\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 2.7111e-06\n",
      "Epoch 2965/3000\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 2.6522e-06\n",
      "Epoch 2966/3000\n",
      "8/8 [==============================] - 0s 9ms/step - loss: 2.6495e-06\n",
      "Epoch 2967/3000\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 2.6469e-06\n",
      "Epoch 2968/3000\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 2.6553e-06\n",
      "Epoch 2969/3000\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 2.6460e-06\n",
      "Epoch 2970/3000\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 2.6441e-06\n",
      "Epoch 2971/3000\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 2.6389e-06\n",
      "Epoch 2972/3000\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 2.6408e-06\n",
      "Epoch 2973/3000\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 2.6421e-06\n",
      "Epoch 2974/3000\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 2.6372e-06\n",
      "Epoch 2975/3000\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 2.6528e-06\n",
      "Epoch 2976/3000\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 2.6641e-06\n",
      "Epoch 2977/3000\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 2.6368e-06\n",
      "Epoch 2978/3000\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 2.6538e-06\n",
      "Epoch 2979/3000\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 2.6509e-06\n",
      "Epoch 2980/3000\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 2.6573e-06\n",
      "Epoch 2981/3000\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 2.6599e-06\n",
      "Epoch 2982/3000\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 2.6399e-06\n",
      "Epoch 2983/3000\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 2.6672e-06\n",
      "Epoch 2984/3000\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 2.7249e-06\n",
      "Epoch 2985/3000\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 2.6510e-06\n",
      "Epoch 2986/3000\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 2.6570e-06\n",
      "Epoch 2987/3000\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 2.6517e-06\n",
      "Epoch 2988/3000\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 2.6476e-06\n",
      "Epoch 2989/3000\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 2.6733e-06\n",
      "Epoch 2990/3000\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 2.6474e-06\n",
      "Epoch 2991/3000\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 2.6387e-06\n",
      "Epoch 2992/3000\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 2.6453e-06\n",
      "Epoch 2993/3000\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 2.6628e-06\n",
      "Epoch 2994/3000\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 2.6574e-06\n",
      "Epoch 2995/3000\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 2.6350e-06\n",
      "Epoch 2996/3000\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 2.6400e-06\n",
      "Epoch 2997/3000\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 2.6409e-06\n",
      "Epoch 2998/3000\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 2.6388e-06\n",
      "Epoch 2999/3000\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 2.6386e-06\n",
      "Epoch 3000/3000\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 2.6631e-06\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x16dabc1fb20>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M = keras.Sequential([\n",
    "    #keras.Input()\n",
    "   # nosed simulation (group I)\n",
    "   ProbLayer(input_shape=(1,),\n",
    "             scale=[0.3,0.3,0.3],\n",
    "             bias=[-20,10,40],\n",
    "             mu=2.85,\n",
    "             sigmaInc=[0.4,0.2,0.1])\n",
    "])\n",
    "\n",
    "M.summary()\n",
    "M.compile(tf.optimizers.Adam(learning_rate=0.0025), loss='mse')\n",
    "M.fit(xI, zI, batch_size=64, epochs=3000)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[array([-0.67740643, -0.44921347, -1.4518938 ], dtype=float32), array([-21.941952,   5.440454,  23.866613], dtype=float32), 3.2714922, array([ 0.18602762,  0.21164154, -0.00136779], dtype=float32)]\n",
      "scale =  [0.36801672 0.46234962 0.16963366]\n",
      "bias =  [-21.941952   5.440454  23.866613]\n",
      "mu =  3.2714922\n",
      "sigma =  [0.19602761626243592, 0.41766915142536165, 0.42766915142536166]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(M.layers[0].get_weights())\n",
    "\n",
    "L = M.layers[0]\n",
    "scale,bias,mu,sInc = L.get_weights()\n",
    "scale = tf.nn.softmax(scale)\n",
    "print(\"scale = \", scale.numpy())\n",
    "print(\"bias = \", bias)\n",
    "print(\"mu = \", mu) \n",
    "S = [sum(np.maximum(sInc[i],0) + 0.01 for i in range(0,id+1)) for id in range(0, len(sInc))]\n",
    "print(\"sigma = \", S)\n",
    "zz = L(xI)\n",
    "zA = sum(logNorm(xI,scale[i],bias[i],mu,S[i]) for i in range(0,3))\n",
    "\n",
    "plt.plot(xI,zA, 'b')\n",
    "plt.plot(xI, yI, 'r')\n",
    "plt.plot(xI, zz, 'g')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.4, 0.4, 0.15]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "csv = pd.read_csv('C:/work/ImageProcDocs/Python-playground/FishPopulation/white.csv')\n",
    "y0 = csv[cols[2]]\n",
    "y = y0 / sum(y0)\n",
    "x0 = list(range(1,len(y)+1,1))\n",
    "fI = scipy.interpolate.interp1d(x0, y, kind='linear')\n",
    "xI = np.linspace(x0[0],x0[-1], 512)\n",
    "yI = fI(xI)\n",
    "zI = gaussian_filter1d(yI, 3, mode='nearest')\n",
    "\n",
    "scale = [0.4,0.4,0.15]\n",
    "#scale = np.array(scale) / sum(y0)\n",
    "print(scale)\n",
    "bias = [0,15,45]\n",
    "mu = 2.5\n",
    "sigma = [0.3,0.42,0.74]\n",
    "\n",
    "zA = sum(logNorm(xI,scale[i],bias[i],mu,sigma[i]) for i in range(0,3))\n",
    "plt.plot(xI, yI)\n",
    "plt.plot(xI,zA)\n",
    "#plt.plot(xI, zI)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_4\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " prob_layer_4 (ProbLayer)    (None, 1)                 10        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 10\n",
      "Trainable params: 10\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Epoch 1/3000\n",
      "8/8 [==============================] - 1s 13ms/step - loss: 1.3023e-05\n",
      "Epoch 2/3000\n",
      "8/8 [==============================] - 0s 12ms/step - loss: 1.0251e-05\n",
      "Epoch 3/3000\n",
      "8/8 [==============================] - 0s 12ms/step - loss: 8.9184e-06\n",
      "Epoch 4/3000\n",
      "8/8 [==============================] - 0s 11ms/step - loss: 8.0221e-06\n",
      "Epoch 5/3000\n",
      "8/8 [==============================] - 0s 11ms/step - loss: 7.4334e-06\n",
      "Epoch 6/3000\n",
      "8/8 [==============================] - 0s 11ms/step - loss: 6.8349e-06\n",
      "Epoch 7/3000\n",
      "8/8 [==============================] - 0s 10ms/step - loss: 6.3264e-06\n",
      "Epoch 8/3000\n",
      "8/8 [==============================] - 0s 11ms/step - loss: 5.8589e-06\n",
      "Epoch 9/3000\n",
      "8/8 [==============================] - 0s 9ms/step - loss: 5.4535e-06\n",
      "Epoch 10/3000\n",
      "8/8 [==============================] - 0s 10ms/step - loss: 5.0662e-06\n",
      "Epoch 11/3000\n",
      "8/8 [==============================] - 0s 10ms/step - loss: 4.7342e-06\n",
      "Epoch 12/3000\n",
      "8/8 [==============================] - 0s 9ms/step - loss: 4.4252e-06\n",
      "Epoch 13/3000\n",
      "8/8 [==============================] - 0s 10ms/step - loss: 4.1330e-06\n",
      "Epoch 14/3000\n",
      "8/8 [==============================] - 0s 9ms/step - loss: 3.8916e-06\n",
      "Epoch 15/3000\n",
      "8/8 [==============================] - 0s 10ms/step - loss: 3.6974e-06\n",
      "Epoch 16/3000\n",
      "8/8 [==============================] - 0s 10ms/step - loss: 3.4736e-06\n",
      "Epoch 17/3000\n",
      "8/8 [==============================] - 0s 10ms/step - loss: 3.2980e-06\n",
      "Epoch 18/3000\n",
      "8/8 [==============================] - 0s 10ms/step - loss: 3.1550e-06\n",
      "Epoch 19/3000\n",
      "8/8 [==============================] - 0s 10ms/step - loss: 3.0138e-06\n",
      "Epoch 20/3000\n",
      "8/8 [==============================] - 0s 10ms/step - loss: 2.8961e-06\n",
      "Epoch 21/3000\n",
      "8/8 [==============================] - 0s 10ms/step - loss: 2.7887e-06\n",
      "Epoch 22/3000\n",
      "8/8 [==============================] - 0s 10ms/step - loss: 2.6986e-06\n",
      "Epoch 23/3000\n",
      "8/8 [==============================] - 0s 10ms/step - loss: 2.6112e-06\n",
      "Epoch 24/3000\n",
      "8/8 [==============================] - 0s 10ms/step - loss: 2.5347e-06\n",
      "Epoch 25/3000\n",
      "8/8 [==============================] - 0s 12ms/step - loss: 2.4767e-06\n",
      "Epoch 26/3000\n",
      "8/8 [==============================] - 0s 10ms/step - loss: 2.4071e-06\n",
      "Epoch 27/3000\n",
      "8/8 [==============================] - 0s 10ms/step - loss: 2.3485e-06\n",
      "Epoch 28/3000\n",
      "8/8 [==============================] - 0s 11ms/step - loss: 2.3043e-06\n",
      "Epoch 29/3000\n",
      "8/8 [==============================] - 0s 10ms/step - loss: 2.2579e-06\n",
      "Epoch 30/3000\n",
      "8/8 [==============================] - 0s 11ms/step - loss: 2.2080e-06\n",
      "Epoch 31/3000\n",
      "8/8 [==============================] - 0s 11ms/step - loss: 2.1802e-06\n",
      "Epoch 32/3000\n",
      "8/8 [==============================] - 0s 10ms/step - loss: 2.1390e-06\n",
      "Epoch 33/3000\n",
      "8/8 [==============================] - 0s 11ms/step - loss: 2.0980e-06\n",
      "Epoch 34/3000\n",
      "8/8 [==============================] - 0s 9ms/step - loss: 2.0695e-06\n",
      "Epoch 35/3000\n",
      "8/8 [==============================] - 0s 12ms/step - loss: 2.0295e-06\n",
      "Epoch 36/3000\n",
      "8/8 [==============================] - 0s 11ms/step - loss: 1.9995e-06\n",
      "Epoch 37/3000\n",
      "8/8 [==============================] - 0s 10ms/step - loss: 1.9698e-06\n",
      "Epoch 38/3000\n",
      "8/8 [==============================] - 0s 11ms/step - loss: 1.9491e-06\n",
      "Epoch 39/3000\n",
      "8/8 [==============================] - 0s 10ms/step - loss: 1.9124e-06\n",
      "Epoch 40/3000\n",
      "8/8 [==============================] - 0s 11ms/step - loss: 1.8866e-06\n",
      "Epoch 41/3000\n",
      "8/8 [==============================] - 0s 11ms/step - loss: 1.8608e-06\n",
      "Epoch 42/3000\n",
      "8/8 [==============================] - 0s 10ms/step - loss: 1.8279e-06\n",
      "Epoch 43/3000\n",
      "8/8 [==============================] - 0s 11ms/step - loss: 1.8094e-06\n",
      "Epoch 44/3000\n",
      "8/8 [==============================] - 0s 11ms/step - loss: 1.7831e-06\n",
      "Epoch 45/3000\n",
      "8/8 [==============================] - 0s 10ms/step - loss: 1.7542e-06\n",
      "Epoch 46/3000\n",
      "8/8 [==============================] - 0s 10ms/step - loss: 1.7280e-06\n",
      "Epoch 47/3000\n",
      "8/8 [==============================] - 0s 10ms/step - loss: 1.7006e-06\n",
      "Epoch 48/3000\n",
      "8/8 [==============================] - 0s 11ms/step - loss: 1.6812e-06\n",
      "Epoch 49/3000\n",
      "8/8 [==============================] - 0s 11ms/step - loss: 1.6534e-06\n",
      "Epoch 50/3000\n",
      "8/8 [==============================] - 0s 11ms/step - loss: 1.6335e-06\n",
      "Epoch 51/3000\n",
      "8/8 [==============================] - 0s 11ms/step - loss: 1.6082e-06\n",
      "Epoch 52/3000\n",
      "8/8 [==============================] - 0s 11ms/step - loss: 1.5811e-06\n",
      "Epoch 53/3000\n",
      "8/8 [==============================] - 0s 11ms/step - loss: 1.5557e-06\n",
      "Epoch 54/3000\n",
      "8/8 [==============================] - 0s 11ms/step - loss: 1.5333e-06\n",
      "Epoch 55/3000\n",
      "8/8 [==============================] - 0s 10ms/step - loss: 1.5212e-06\n",
      "Epoch 56/3000\n",
      "8/8 [==============================] - 0s 10ms/step - loss: 1.4950e-06\n",
      "Epoch 57/3000\n",
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      "8/8 [==============================] - 0s 11ms/step - loss: 2.2285e-07\n",
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      "8/8 [==============================] - 0s 11ms/step - loss: 2.2136e-07\n",
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      "8/8 [==============================] - 0s 11ms/step - loss: 2.2359e-07\n",
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      "8/8 [==============================] - 0s 8ms/step - loss: 2.2501e-07\n",
      "Epoch 2912/3000\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 2.2932e-07\n",
      "Epoch 2913/3000\n",
      "8/8 [==============================] - 0s 11ms/step - loss: 2.2034e-07\n",
      "Epoch 2914/3000\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 2.2380e-07\n",
      "Epoch 2915/3000\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 2.2518e-07\n",
      "Epoch 2916/3000\n",
      "8/8 [==============================] - 0s 9ms/step - loss: 2.2161e-07\n",
      "Epoch 2917/3000\n",
      "8/8 [==============================] - 0s 11ms/step - loss: 2.2533e-07\n",
      "Epoch 2918/3000\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 2.2517e-07\n",
      "Epoch 2919/3000\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 2.2311e-07\n",
      "Epoch 2920/3000\n",
      "8/8 [==============================] - 0s 11ms/step - loss: 2.2146e-07\n",
      "Epoch 2921/3000\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 2.2645e-07\n",
      "Epoch 2922/3000\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 2.2641e-07\n",
      "Epoch 2923/3000\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 2.2552e-07\n",
      "Epoch 2924/3000\n",
      "8/8 [==============================] - 0s 11ms/step - loss: 2.2392e-07\n",
      "Epoch 2925/3000\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 2.3238e-07\n",
      "Epoch 2926/3000\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 2.2322e-07\n",
      "Epoch 2927/3000\n",
      "8/8 [==============================] - 0s 9ms/step - loss: 2.2656e-07\n",
      "Epoch 2928/3000\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 2.2454e-07\n",
      "Epoch 2929/3000\n",
      "8/8 [==============================] - 0s 9ms/step - loss: 2.2508e-07\n",
      "Epoch 2930/3000\n",
      "8/8 [==============================] - 0s 10ms/step - loss: 2.2346e-07\n",
      "Epoch 2931/3000\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 2.2150e-07\n",
      "Epoch 2932/3000\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 2.2270e-07\n",
      "Epoch 2933/3000\n",
      "8/8 [==============================] - 0s 9ms/step - loss: 2.2010e-07\n",
      "Epoch 2934/3000\n",
      "8/8 [==============================] - 0s 11ms/step - loss: 2.2132e-07\n",
      "Epoch 2935/3000\n",
      "8/8 [==============================] - 0s 9ms/step - loss: 2.2197e-07\n",
      "Epoch 2936/3000\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 2.2134e-07\n",
      "Epoch 2937/3000\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 2.2380e-07\n",
      "Epoch 2938/3000\n",
      "8/8 [==============================] - 0s 11ms/step - loss: 2.2294e-07\n",
      "Epoch 2939/3000\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 2.2404e-07\n",
      "Epoch 2940/3000\n",
      "8/8 [==============================] - 0s 9ms/step - loss: 2.2732e-07\n",
      "Epoch 2941/3000\n",
      "8/8 [==============================] - 0s 11ms/step - loss: 2.2333e-07\n",
      "Epoch 2942/3000\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 2.2285e-07\n",
      "Epoch 2943/3000\n",
      "8/8 [==============================] - 0s 9ms/step - loss: 2.2329e-07\n",
      "Epoch 2944/3000\n",
      "8/8 [==============================] - 0s 12ms/step - loss: 2.2148e-07\n",
      "Epoch 2945/3000\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 2.2367e-07\n",
      "Epoch 2946/3000\n",
      "8/8 [==============================] - 0s 9ms/step - loss: 2.2096e-07\n",
      "Epoch 2947/3000\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 2.2509e-07\n",
      "Epoch 2948/3000\n",
      "8/8 [==============================] - 0s 9ms/step - loss: 2.2225e-07\n",
      "Epoch 2949/3000\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 2.2270e-07\n",
      "Epoch 2950/3000\n",
      "8/8 [==============================] - 0s 12ms/step - loss: 2.3203e-07\n",
      "Epoch 2951/3000\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 2.3632e-07\n",
      "Epoch 2952/3000\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 2.2459e-07\n",
      "Epoch 2953/3000\n",
      "8/8 [==============================] - 0s 9ms/step - loss: 2.2655e-07\n",
      "Epoch 2954/3000\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 2.2355e-07\n",
      "Epoch 2955/3000\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 2.2449e-07\n",
      "Epoch 2956/3000\n",
      "8/8 [==============================] - 0s 11ms/step - loss: 2.2158e-07\n",
      "Epoch 2957/3000\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 2.3504e-07\n",
      "Epoch 2958/3000\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 2.3974e-07\n",
      "Epoch 2959/3000\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 2.4362e-07\n",
      "Epoch 2960/3000\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 2.3136e-07\n",
      "Epoch 2961/3000\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 2.3840e-07\n",
      "Epoch 2962/3000\n",
      "8/8 [==============================] - 0s 12ms/step - loss: 2.3209e-07\n",
      "Epoch 2963/3000\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 2.2112e-07\n",
      "Epoch 2964/3000\n",
      "8/8 [==============================] - 0s 9ms/step - loss: 2.2581e-07\n",
      "Epoch 2965/3000\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 2.2090e-07\n",
      "Epoch 2966/3000\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 2.2232e-07\n",
      "Epoch 2967/3000\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 2.2124e-07\n",
      "Epoch 2968/3000\n",
      "8/8 [==============================] - 0s 11ms/step - loss: 2.2089e-07\n",
      "Epoch 2969/3000\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 2.2151e-07\n",
      "Epoch 2970/3000\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 2.2334e-07\n",
      "Epoch 2971/3000\n",
      "8/8 [==============================] - 0s 12ms/step - loss: 2.2304e-07\n",
      "Epoch 2972/3000\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 2.2436e-07\n",
      "Epoch 2973/3000\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 2.2381e-07\n",
      "Epoch 2974/3000\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 2.2283e-07\n",
      "Epoch 2975/3000\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 2.2348e-07\n",
      "Epoch 2976/3000\n",
      "8/8 [==============================] - 0s 9ms/step - loss: 2.2547e-07\n",
      "Epoch 2977/3000\n",
      "8/8 [==============================] - 0s 12ms/step - loss: 2.2253e-07\n",
      "Epoch 2978/3000\n",
      "8/8 [==============================] - 0s 9ms/step - loss: 2.2615e-07\n",
      "Epoch 2979/3000\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 2.2296e-07\n",
      "Epoch 2980/3000\n",
      "8/8 [==============================] - 0s 9ms/step - loss: 2.2584e-07\n",
      "Epoch 2981/3000\n",
      "8/8 [==============================] - 0s 9ms/step - loss: 2.2229e-07\n",
      "Epoch 2982/3000\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 2.2238e-07\n",
      "Epoch 2983/3000\n",
      "8/8 [==============================] - 0s 10ms/step - loss: 2.2496e-07\n",
      "Epoch 2984/3000\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 2.2466e-07\n",
      "Epoch 2985/3000\n",
      "8/8 [==============================] - 0s 10ms/step - loss: 2.2411e-07\n",
      "Epoch 2986/3000\n",
      "8/8 [==============================] - 0s 11ms/step - loss: 2.2099e-07\n",
      "Epoch 2987/3000\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 2.2385e-07\n",
      "Epoch 2988/3000\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 2.2363e-07\n",
      "Epoch 2989/3000\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 2.2447e-07\n",
      "Epoch 2990/3000\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 2.2151e-07\n",
      "Epoch 2991/3000\n",
      "8/8 [==============================] - 0s 12ms/step - loss: 2.2343e-07\n",
      "Epoch 2992/3000\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 2.2132e-07\n",
      "Epoch 2993/3000\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 2.2296e-07\n",
      "Epoch 2994/3000\n",
      "8/8 [==============================] - 0s 9ms/step - loss: 2.2161e-07\n",
      "Epoch 2995/3000\n",
      "8/8 [==============================] - 0s 9ms/step - loss: 2.2322e-07\n",
      "Epoch 2996/3000\n",
      "8/8 [==============================] - 0s 10ms/step - loss: 2.2673e-07\n",
      "Epoch 2997/3000\n",
      "8/8 [==============================] - 0s 11ms/step - loss: 2.3155e-07\n",
      "Epoch 2998/3000\n",
      "8/8 [==============================] - 0s 9ms/step - loss: 2.2474e-07\n",
      "Epoch 2999/3000\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 2.2337e-07\n",
      "Epoch 3000/3000\n",
      "8/8 [==============================] - 0s 9ms/step - loss: 2.2296e-07\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x16daba27850>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M = keras.Sequential([\n",
    "   # white simulation (group II)\n",
    "   ProbLayer(input_shape=(1,),\n",
    "              scale=[0.4,0.4,0.15],\n",
    "              bias=[0,15,45],\n",
    "              mu=2.5,\n",
    "              sigmaInc=[0.3,0.11,0.31])\n",
    "])\n",
    "\n",
    "M.summary()\n",
    "M.compile(tf.optimizers.Adam(learning_rate=0.0025), loss='mse')\n",
    "M.fit(xI, zI, batch_size=64, epochs=3000)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[array([-0.6687262 , -0.91056013, -2.5497875 ], dtype=float32), array([ 1.7345766, 19.996706 , 45.326385 ], dtype=float32), 2.488845, array([0.4723609 , 0.23602544, 0.18346466], dtype=float32)]\n",
      "scale =  [0.51609844 0.40523356 0.07866798]\n",
      "bias =  [ 1.7345766 19.996706  45.326385 ]\n",
      "mu =  2.488845\n",
      "sigma =  [0.48236090898513795, 0.7283863466978073, 0.921851007938385]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(M.layers[0].get_weights())\n",
    "\n",
    "L = M.layers[0]\n",
    "scale,bias,mu,sInc = L.get_weights()\n",
    "scale = tf.nn.softmax(scale)\n",
    "print(\"scale = \", scale.numpy())\n",
    "print(\"bias = \", bias)\n",
    "print(\"mu = \", mu) \n",
    "S = [sum(np.maximum(sInc[i],0) + 0.01 for i in range(0,id+1)) for id in range(0, len(sInc))]\n",
    "print(\"sigma = \", S)\n",
    "zz = L(xI)\n",
    "zA = sum(logNorm(xI,scale[i],bias[i],mu,S[i]) for i in range(0,3))\n",
    "\n",
    "plt.plot(xI,zA, 'b')\n",
    "plt.plot(xI, yI, 'r')\n",
    "plt.plot(xI, zz, 'g')\n",
    "plt.show()"
   ]
  }
 ],
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