diff --git "a/\344\275\234\344\270\2324-2019300724-\344\275\225\350\231\216\344\274\237" "b/\344\275\234\344\270\2324-2019300724-\344\275\225\350\231\216\344\274\237"
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+import numpy as np
+import matplotlib.pyplot as plt
+from scipy.io import loadmat
+from scipy.optimize import minimize
+from sklearn.metrics import classification_report
+
+"""
+每个训练样本是一个20x20的图像
+原始数据是一个字典，字典中的X的shape是(5000,400)，y的shape是(5000,1)
+X的每一行代表一个数字图像的特征向量(400维，一个像素占一维)
+y(1，2，3···9，10)代表数字的值(1，2，3···9，0)，共有5000个训练样本
+"""
+
+
+# 获取原始数据
+def raw_data(path):
+    data=loadmat(path)
+    return data
+
+
+# 随机生成一张图片
+def random_an_image(x,y):
+    index=np.random.randint(0,5000)
+    image=x[index]
+    fig,ax=plt.subplots(1,1,figsize=(20,20))
+    ax.matshow(image.reshape(20,20),cmap='gray_r')
+    print("this is",y[index])
+    plt.show()
+
+
+# 随机生成100张图片
+def random_100_image(x):
+    indexs=np.random.choice(x.shape[0],100)
+    images=x[indexs]
+    # 将显示幕布分成10行10列，每一份大小是20x20
+    fig,axs=plt.subplots(10,10,figsize=(20,20))
+    for row in range(10):
+        for col in range(10):
+            axs[row,col].matshow(images[row*10+col].reshape(20,20),cmap='gray_r')
+    plt.show()
+
+
+def sigmoid(z):
+    return 1/(1+np.exp(-z))
+
+
+# 代价函数
+def cost_function(theta,x,y,lam):
+    m=x.shape[0]
+    first=(y.dot(np.log(sigmoid(x.dot(theta))))+(1-y).dot(np.log(1-sigmoid(x.dot(theta)))))/(-m)
+    theta_reg=theta[1:]  # 不惩罚第一项
+    second=lam*(theta_reg.dot(theta_reg))/(2*m)
+    j=first+second
+    return j
+
+
+# 梯度下降，只需求偏导数即可
+def gradient_descent(theta,x,y,lam):
+    m=x.shape[0]
+    partial_first=((sigmoid(x.dot(theta))-y).T).dot(x)/m
+    partial_second=lam*theta/m
+    partial_second[0]=0
+    partial=partial_first+partial_second
+    return partial
+
+
+# 生成theta矩阵，其中每一行代表一个数字的theta向量（401维），一共10个数字，所以一共10行
+def one_to_all(x,y,lam,k):
+    theta_all=np.zeros((k,x.shape[1]))
+    # print(theta_all.shape)  #(10,401)
+    for i in range(1,k+1):
+        # 获取每一个数字的y向量
+        y_i=np.array([1 if num==i else 0 for num in y])
+        # 逐个训练theta向量
+        theta_i=theta_all[i-1]
+        result=minimize(fun=cost_function,args=(x,y_i,lam),x0=theta_i,method='tnc',jac=gradient_descent)
+        # 合并theta向量
+        theta_all[i-1]=result.x
+    return theta_all
+
+
+# 获得预测结果矩阵
+def predict(theta_all,x):
+    h=sigmoid(x.dot(theta_all.T))
+    h_max=np.argmax(h,axis=1)  # 获取h每一行的最大值元素下标
+    # 因为下标从0开始，所以加1
+    h_max=h_max+1
+    # print(h_max.shape)  # (5000,)
+    return h_max
+
+
+def main():
+    rawdata=raw_data('venv/lib/dataset/ex3data1.mat')
+    x1=rawdata['X']
+    x=np.c_[np.ones(x1.shape[0]),x1]
+    y=rawdata['y'].ravel()  # 降维，方便之后计算
+    # print(x.shape,y.shape)  # (5000, 401) (5000,)
+    # random_an_image(x1, y)
+    random_100_image(x1)
+    theta_all=one_to_all(x,y,1,10)
+    y_predict=predict(theta_all,x)
+    report=classification_report(y_predict,y)
+    print(report)
+
+
+main()
+
+import numpy as np
+from scipy.io import loadmat
+from sklearn.metrics import classification_report
+"""
+不需要计算权值，仅做前向传播
+"""
+
+
+def raw_data(path):
+    data=loadmat(path)
+    return data
+
+
+def sigmoid(z):
+    return 1/(1+np.exp(-z))
+
+
+def predict(y):
+    h_max=np.argmax(y,axis=1)
+    h_max=h_max+1
+    return h_max
+
+
+def main():
+    rawdata=raw_data('venv/lib/dataset/ex3data1.mat')
+    weight=raw_data('venv/lib/dataset/ex3weights.mat')
+    x1=rawdata['X']
+    x=np.c_[np.ones(x1.shape[0]),x1]
+    y=rawdata['y'].ravel()
+    theta1=weight['Theta1']
+    theta2 = weight['Theta2']
+    # print(x.shape,y.shape,theta1.shape,theta2.shape)  # (5000, 401) (5000,) (25, 401) (10, 26)
+    a1=x
+    z2=a1.dot(theta1.T)
+    a2=sigmoid(z2)
+    a2=np.c_[np.ones(a2.shape[0]),a2]
+    z3=a2.dot(theta2.T)
+    a3=sigmoid(z3)
+    report=classification_report(predict(a3),y)
+    print(report)
+
+
+main()
+