Merge branch 'Danielyan86-master'

python3/README.md

@@ -1 +1,9 @@
-Refactor the code with python3
+## refactor with python3
+## Classify iris 
+- problem description：https://en.wikipedia.org/wiki/Iris_flower_data_set
+- data source: http://archive.ics.uci.edu/ml/datasets/Iris
+
+## 使用python3重构
+## 鸢尾花分类问题
+- 问题描述：https://en.wikipedia.org/wiki/Iris_flower_data_set
+- 数据集来源 http://archive.ics.uci.edu/ml/datasets/Iris

rbm.py

@@ -0,0 +1,41 @@
+#!/usr/bin/env python
+# -*- coding: UTF-8 -*-
+
+from __future__ import print_function
+
+import numpy as np
+
+
+def sigmoid(x):
+    return 1.0 / (1.0 + np.exp(-x))
+
+
+class RBMLayer(object):
+    def __init__(self, visible_size, hidden_size):
+        self.visible_size = visible_size
+        self.hidden_size = hidden_size
+        self.W = self.initialize_weights()
+        self.b = self.initialize_visible_bias()
+        self.c = self.initialize_hidden_bias()
+
+    def initialize_weights(self):
+        return np.random.normal(scale=0.01,
+                                size=(self.visible_size, self.hidden_size))
+
+    def initialize_visible_bias(self):
+        return np.zeros(self.visible_size)
+
+    def initialize_hidden_bias(self):
+        return np.zeros(self.hidden_size)
+
+    # 输入观测变量v, 输出隐变量的值h
+    def forward(self, v):
+        return sigmoid(np.dot(v, self.W) + self.c)
+
+    # 输入隐变量的值h, 输出观测变量值y
+    def backward(self, h):
+        return sigmoid(np.dot(h, self.W.T) + self.b)
+
+    # k-step contrastive divergence 训练算法
+    def contrastive_divergence(self, k, data_set):
+        pass