From c22ea4e94319dce984b806c0738e003845ab8b12 Mon Sep 17 00:00:00 2001
From: Vicolas11 <46172140+Vicolas11@users.noreply.github.com>
Date: Wed, 22 Apr 2020 02:25:48 +0100
Subject: [PATCH] Create Akoh_Victor_Ojodom

---
 Akoh_Victor_Ojodom | 66 ++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 66 insertions(+)
 create mode 100644 Akoh_Victor_Ojodom

diff --git a/Akoh_Victor_Ojodom b/Akoh_Victor_Ojodom
new file mode 100644
index 0000000..4fca059
--- /dev/null
+++ b/Akoh_Victor_Ojodom
@@ -0,0 +1,66 @@
+import numpy as np
+
+class LogisticRegression:
+    def __init__(self, lr=0.01, num_iter=100000, fit_intercept=True, verbose=False):
+        self.lr = lr
+        self.num_iter = num_iter
+        self.fit_intercept = fit_intercept
+        self.verbose = verbose
+               
+    def sigmoid(self, z):
+        return 1 / (1 + np.exp(-z))
+    
+    def addIntercept(self, X):
+        intercept = np.ones((X.shape[0], 1))
+        return np.concatenate((intercept, X), axis=1)  
+    
+    def loss(self, h, y):
+        return (-y * np.log(h) - (1 - y) * np.log(1 - h)).mean()
+    
+    def fit(self, X, y):
+        if self.fit_intercept:
+            X = self.addIntercept(X)
+        
+        self.theta = np.zeros(X.shape[1])
+        
+        for i in range(self.num_iter):
+            z = np.dot(X, self.theta)
+            h = self.sigmoid(z)
+            gradient = np.dot(X.T, (h - y)) / y.size
+            self.theta -= self.lr * gradient
+            
+            z = np.dot(X, self.theta)
+            h = self.sigmoid(z)
+            loss = self.loss(h, y)
+            
+            if(self.verbose == True and i % 10000 == 0):
+                print('Loss: {} \t'.format(loss))
+    
+    def predictProb(self, X):
+        if self.fit_intercept:
+            X = self.addIntercept(X)
+    
+        return self.sigmoid(np.dot(X, self.theta))
+    
+    def predict(self, X):
+        return self.predictProb(X).round()
+
+#Evaluating Using Logistic Regression created
+mdl = LogisticRegression(lr=0.1, num_iter=300000)
+
+from sklearn.datasets import load_iris
+iris = load_iris()
+X = iris.data[:, :3]
+y = (iris.target != 0) * 1
+mdl.fit(X, y)
+pred = mdl.predict(X)
+(pred == y).mean()
+mdl.theta
+
+#Evaluating Using Default/In-built Logistic Regression from sklearn
+from sklearn.linear_model import LogisticRegression
+md = LogisticRegression()
+md.fit(X,y)
+md.predict(X)
+md.coef_
+md.intercept_