self study data science -  AI .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "unique-richardson",
   "metadata": {},
   "source": [
    "# Self study data science and AI"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "wanted-storm",
   "metadata": {},
   "source": [
    "This document contains an overview of topics to study related to data science and AI\n",
    "The Github repository https://github.com/defreeze/data_science_self_study "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "handmade-newspaper",
   "metadata": {},
   "outputs": [
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     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "# import libraries\n",
    "# https://jakevdp.github.io/blog/2017/12/05/installing-python-packages-from-jupyter/\n",
    "import sys\n",
    "!{sys.executable} -m pip install pandas\n",
    "!{sys.executable} -m pip install matplotlib\n",
    "!{sys.executable} -m pip install tensorflow\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "touched-palestine",
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://rubikscode.net/2018/10/22/implementing-restricted-boltzmann-machine-with-python-and-tensorflow/\n",
    "class RBM(object):\n",
    "    def __init__(self, visible_dim, hidden_dim, learning_rate, number_of_iterations):\n",
    "        \n",
    "        self._graph = tf.Graph()\n",
    "        \n",
    "        #Initialize graph\n",
    "        with self._graph.as_default():\n",
    "            \n",
    "            self._num_iter = number_of_iterations\n",
    "            self._visible_biases = tf.Variable(tf.random_uniform([1, visible_dim], 0, 1, name = \"visible_biases\"))\n",
    "            self._hidden_biases = tf.Variable(tf.random_uniform([1, hidden_dim], 0, 1, name = \"hidden_biases\"))\n",
    "            self._hidden_states = tf.Variable(tf.zeros([1, hidden_dim], tf.float32, name = \"hidden_biases\"))\n",
    "            self._visible_cdstates = tf.Variable(tf.zeros([1, visible_dim], tf.float32, name = \"visible_biases\"))\n",
    "            self._hidden_cdstates = tf.Variable(tf.zeros([1, hidden_dim], tf.float32, name = \"hidden_biases\"))\n",
    "            self._weights = tf.Variable(tf.random_normal([visible_dim, hidden_dim], 0.01), name=\"weights\")\n",
    "            self._leraning_rate =  tf.Variable(tf.fill([visible_dim, hidden_dim], learning_rate), name = \"learning_rate\")\n",
    "            \n",
    "            self._input_sample = tf.placeholder(tf.float32, [visible_dim], name = \"input_sample\")\n",
    "            \n",
    "            # Gibbs Sampling\n",
    "            input_matrix = tf.transpose(tf.stack([self._input_sample for i in range(hidden_dim)]))\n",
    "            _hidden_probabilities = tf.sigmoid(tf.add(tf.multiply(input_matrix, self._weights), tf.stack([self._hidden_biases[0] for i in range(visible_dim)])))\n",
    "            self._hidden_states = self.callculate_state(_hidden_probabilities)\n",
    "            _visible_probabilities = tf.sigmoid(tf.add(tf.multiply(self._hidden_states, self._weights), tf.transpose(tf.stack([self._visible_biases[0] for i in range(hidden_dim)]))))\n",
    "            self._visible_cdstates = self.callculate_state(_visible_probabilities)\n",
    "            self._hidden_cdstates = self.callculate_state(tf.sigmoid(tf.multiply(self._visible_cdstates, self._weights) + self._hidden_biases))\n",
    "            \n",
    "            #CD\n",
    "            positive_gradient_matrix = tf.multiply(input_matrix, self._hidden_states)\n",
    "            negative_gradient_matrix = tf.multiply(self._visible_cdstates, self._hidden_cdstates)\n",
    "            \n",
    "            new_weights = self._weights\n",
    "            new_weights.assign_add(tf.multiply(positive_gradient_matrix, self._leraning_rate))\n",
    "            new_weights.assign_sub(tf.multiply(negative_gradient_matrix, self._leraning_rate))\n",
    "\n",
    "            self._training = tf.assign(self._weights, new_weights) \n",
    "            \n",
    "            #Initilize session and run it\n",
    "            self._sess = tf.Session()\n",
    "            initialization = tf.global_variables_initializer()\n",
    "            self._sess.run(initialization)\n",
    "        \n",
    "    def train(self, input_vects):\n",
    "        for iter_no in range(self._num_iter):\n",
    "            for input_vect in input_vects:\n",
    "                self._sess.run(self._training,\n",
    "                               feed_dict={self._input_sample: input_vect})\n",
    "    \n",
    "    def callculate_state(self, probability):\n",
    "        return tf.floor(probability + tf.random_uniform(tf.shape(probability), 0, 1))\n",
    "        \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "filled-briefing",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
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     ]
    },
    {
     "ename": "TypeError",
     "evalue": "__init__() takes 4 positional arguments but 5 were given",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-21-4454bd327770>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mtest\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mrbm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mRBM\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m100\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0mrbm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: __init__() takes 4 positional arguments but 5 were given"
     ]
    }
   ],
   "source": [
    "!{sys.executable} -m pip install RBM\n",
    "\n",
    "\n",
    "test = np.array([[0,1,1,0], [0,1,0,0], [0,0,1,1]])\n",
    "rbm = RBM(4, 3, 0.1, 100)\n",
    "rbm.train(test)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "hidden-gibson",
   "metadata": {},
   "source": [
    "# header info\n",
    "\n",
    "Restricted Boltzmann Machines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "perfect-hearing",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  one two three\n",
      "1  a1  b1    c1\n",
      "2  a2  b2    c2\n",
      "3  a3  b3    c3\n"
     ]
    }
   ],
   "source": [
    "array = np.array([['a1', 'b1', 'c1'],\n",
    "                  ['a2', 'b2', 'c2'],\n",
    "                  ['a3', 'b3', 'c3']])\n",
    "\n",
    "columns = ['one', 'two', 'three']\n",
    "index = [1, 2, 3]\n",
    "\n",
    "df = pd.DataFrame(array, index, columns)\n",
    "print (df)"
   ]
  }
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