Merge pull request #140 from jeffin07/master

Logistic Regression

Author: ionicc

Machine_Learning/logisticRegression.py

@@ -0,0 +1,56 @@
+from sklearn.datasets import load_iris
+from sklearn.model_selection import train_test_split
+import numpy as np
+
+
+
+class LogisticRegression:
+	def __init__(self, learning_rate, epochs):
+		self.learning_rate = learning_rate
+		self.epochs = int(epochs)
+
+
+	def sigmoid(self, z):
+		return 1 / (1 + np.exp(-z))
+
+
+	def loss(self, h, y):
+		return (-y * np.log(h) -  (1-y) * np.log(1 - h)).mean()
+
+
+	def fit(self, x, y):
+		y=np.array(y)
+		self.w=np.random.randn(x.shape[1])
+		for i in range(self.epochs):
+			z = np.dot(x,self.w)
+			h=self.sigmoid(z)
+			difference = np.dot(x.T,(h - y ))/len(y)
+			self.w -= self.learning_rate*difference
+			if i % 10 ==0:
+				lo=self.loss(h, y)
+				print("Loss at {} epoch is {} ".format(i,lo))
+		return self.w
+
+
+	def predict(self, x):
+		return(self.sigmoid(np.dot(x, self.w)))
+
+#Data preprocess
+iris = load_iris()
+x = iris.data[0:100, :2]
+y = []
+for i in iris.target:
+	if i==0 or i==1:
+		y.append(i)
+
+model = LogisticRegression(0.1,1000)
+x_train,x_test, y_train, y_test=train_test_split(x , y)
+model.fit(x_train , y_train)
+y_pred = model.predict(x_test)
+for i in range(len(y_pred)):
+	if y_pred[i] > 0.5:
+		y_pred[i]=1
+	else:
+		y_pred[i]=0
+y_test=np.array(y_test)
+print("Accuracy is {} ".format((y_test == y_pred ).mean()*100))