Add LR

Author: rosefun

逻辑斯谛回归/LogisticRegression.py

@@ -0,0 +1,60 @@
+import sklearn.datasets as datasets
+import numpy as np
+from sklearn.linear_model import LogisticRegression as LR
+class LogisticRegression():
+    def __init__(self,alpha=0.01,epochs=3):
+        self.W = None 
+        self.b = None
+        self.alpha = alpha
+        self.epochs = epochs
+    def fit(self,X,y): 
+        # 设定种子
+        np.random.seed(10)
+        self.W = np.random.normal(size=(X.shape[1]))
+        self.b = 0
+        for epoch in range(self.epochs):
+            if epoch%50 == 0:
+                print("epoch",epoch)
+            w_derivate = np.zeros_like(self.W)
+            b_derivate = 0
+            for i in range(len(y)):
+                w_derivate += (y[i] - 1/(1+np.exp(-np.dot(X[i],self.W.T)-self.b)))*X[i]
+                b_derivate += (y[i] - 1/(1+np.exp(-np.dot(X[i],self.W.T)-self.b)))
+            self.W = self.W + self.alpha*np.mean(w_derivate,axis=0)
+            self.b = self.b + self.alpha*np.mean(b_derivate)
+        return self
+    def predict(self,X):
+        p_1 = 1/(1 + np.exp(-np.dot(X,self.W) - self.b))
+        return np.where(p_1>0.5, 1, 0)
+def accuracy(pred, true):
+    count = 0
+    for i in range(len(pred)):
+        if(pred[i] == true[i]):
+            count += 1
+    return count/len(pred)
+def normalize(x):
+    return (x - np.min(x))/(np.max(x) - np.min(x))
+
+if __name__ == "__main__":
+    # input datasets 
+    digits = datasets.load_breast_cancer()
+    X = digits.data
+    y = digits.target
+    # 归一化
+    X_norm = normalize(X)
+    X_train = X_norm[:int(len(X_norm)*0.8)]
+    X_test = X_norm[int(len(X_norm)*0.8):]
+    y_train = y[:int(len(X_norm)*0.8)]
+    y_test = y[int(len(X_norm)*0.8):]
+    # model 1
+    lr = LogisticRegression(epochs=500,alpha=0.03)
+    lr.fit(X_train,y_train)
+    y_pred = lr.predict(X_test)
+    # 评估准确率
+    acc = accuracy(y_pred, y_test)
+    print("acc", acc)
+    # model 2
+    clf_lr = LR()
+    clf_lr.fit(X_train, y_train)
+    y_pred2 = clf_lr.predict(X_test)
+    print("acc2", accuracy(y_pred2, y_test))