bayes

Author: WenDesi

naive_bayes/naive_bayes.py

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+#encoding=utf-8
+
+import pandas as pd
+import numpy as np
+import cv2
+import random
+import time
+
+from sklearn.cross_validation import train_test_split
+from sklearn.metrics import accuracy_score
+
+# 二值化
+def binaryzation(img):
+    cv_img = img.astype(np.uint8)
+    cv2.threshold(cv_img,50,1,cv2.cv.CV_THRESH_BINARY_INV,cv_img)
+    return cv_img
+
+def Train(trainset,train_labels):
+    prior_probability = np.zeros(class_num)                         # 先验概率
+    conditional_probability = np.zeros((class_num,feature_len,2))   # 条件概率
+
+    # 计算先验概率及条件概率
+    for i in range(len(train_labels)):
+        img = binaryzation(trainset[i])     # 图片二值化
+        label = train_labels[i]
+
+        prior_probability[label] += 1
+
+        for j in range(feature_len):
+            conditional_probability[label][j][img[j]] += 1
+
+    # 将概率归到[1.10001]
+    for i in range(class_num):
+        for j in range(feature_len):
+
+            # 经过二值化后图像只有0，1两种取值
+            pix_0 = conditional_probability[i][j][0]
+            pix_1 = conditional_probability[i][j][1]
+
+            # 计算0，1像素点对应的条件概率
+            probalility_0 = (float(pix_0)/float(pix_0+pix_1))*1000000 + 1
+            probalility_1 = (float(pix_1)/float(pix_0+pix_1))*1000000 + 1
+
+            conditional_probability[i][j][0] = probalility_0
+            conditional_probability[i][j][1] = probalility_1
+
+    return prior_probability,conditional_probability
+
+# 计算概率
+def calculate_probability(img,label):
+    probability = int(prior_probability[label])
+
+    for i in range(len(img)):
+        probability *= int(conditional_probability[label][i][img[i]])
+
+    return probability
+
+def Predict(testset,prior_probability,conditional_probability):
+    predict = []
+
+    for img in testset:
+
+        # 图像二值化
+        img = binaryzation(img)
+
+        max_label = 0
+        max_probability = calculate_probability(img,0)
+
+        for j in range(1,10):
+            probability = calculate_probability(img,j)
+
+            if max_probability < probability:
+                max_label = j
+                max_probability = probability
+
+        predict.append(max_label)
+
+    return np.array(predict)
+
+
+class_num = 10
+feature_len = 784
+
+if __name__ == '__main__':
+
+    print 'Start read data'
+
+    time_1 = time.time()
+
+    raw_data = pd.read_csv('../data/train.csv',header=0)
+    data = raw_data.values
+
+    imgs = data[0::,1::]
+    labels = data[::,0]
+
+    # 选取 2/3 数据作为训练集， 1/3 数据作为测试集
+    train_features, test_features, train_labels, test_labels = train_test_split(imgs, labels, test_size=0.33, random_state=23323)
+    # print train_features.shape
+    # print train_features.shape
+
+    time_2 = time.time()
+    print 'read data cost ',time_2 - time_1,' second','\n'
+
+    print 'Start training'
+    prior_probability,conditional_probability = Train(train_features,train_labels)
+    time_3 = time.time()
+    print 'training cost ',time_3 - time_2,' second','\n'
+
+    print 'Start predicting'
+    test_predict = Predict(test_features,prior_probability,conditional_probability)
+    time_4 = time.time()
+    print 'predicting cost ',time_4 - time_3,' second','\n'
+
+    score = accuracy_score(test_labels,test_predict)
+    print "The accruacy socre is ", score