:: coding

Author: lawlite19

AnomalyDetection/AnomalyDetection.py

@@ -1,3 +1,61 @@
 #-*- coding: utf-8 -*-
 # Author: Bob
 # Date:   2016.12.22
+import numpy as np
+from matplotlib import pyplot as plt
+from scipy import io as spio
+
+'''异常检测主运行程序'''
+def anomalyDetection_example():
+    '''加载并显示数据'''
+    data = spio.loadmat('data1.mat')
+    X = data['X']
+    plt = display_2d_data(X, 'bx')
+    plt.title("origin data")
+    plt.show()
+    
+    mu,sigma2 = estimateGaussian(X)
+    print mu,sigma2
+    p = multivariateGaussian(X,mu,sigma2)
+    print p
+    
+    visualizeFit(X,mu,sigma2)
+    
+    
+    
+# 显示二维数据    
+def display_2d_data(X,marker):
+    plt.plot(X[:,0],X[:,1],marker)
+    plt.axis('square')
+    return plt
+
+# 参数估计函数（就是求均值和方差）
+def estimateGaussian(X):
+    m,n = X.shape
+    mu = np.zeros((n,1))
+    sigma2 = np.zeros((n,1))
+    
+    mu = np.mean(X, axis=0) # axis=0表示列，每列的均值
+    sigma2 = np.var(X,axis=0) # 求每列的方差
+    return mu,sigma2
+   
+# 多元高斯分布函数    
+def multivariateGaussian(X,mu,Sigma2):
+    k = len(mu)
+    if (Sigma2.shape[0]>1):
+        Sigma2 = np.diag(Sigma2)
+        
+    X = X-mu
+    argu = (2*np.pi)**(-k/2)*np.linalg.det(Sigma2)**(-0.5)
+    p = argu*np.exp(-0.5*np.sum(np.dot(X,np.linalg.inv(Sigma2))*X,axis=1))  # axis表示每行
+    return p
+    
+# 可视化边界
+def visualizeFit(X,mu,sigma2):
+    X1,X2 = np.meshgrid(0,0.5,35)
+    Z = multivariateGaussian(np.vstack((X1,X2)), mu, Sigma2)
+
+
+if __name__ == '__main__':
+    anomalyDetection_example()
+    

PCA/PCA.py

@@ -83,7 +83,7 @@ def featureNormalize(X):
     mu = np.zeros((1,n));
     sigma = np.zeros((1,n))
 
-    mu = np.mean(X,axis=0)
+    mu = np.mean(X,axis=0)   # axis=0表示列
     sigma = np.std(X,axis=0)
     for i in range(n):
         X[:,i] = (X[:,i]-mu[i])/sigma[i]