diff --git a/Lesson 3/test.py b/Lesson 3/test.py
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+++ b/Lesson 3/test.py	
@@ -0,0 +1,104 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Wed Nov 22 15:15:48 2017
+
+@author: mvkoroteev
+"""
+import scipy as sp
+import pandas
+import matplotlib.pyplot as plt
+
+data = pandas.read_csv("data/seeds.tsv", sep='\t', header=0).as_matrix()
+
+X = data[:,:7]
+Y = data[:, 7]
+
+class hypothesis2(object):
+    def __init__(self, n=2):
+        """ Параметры регрессии """
+        self.theta = [0] * (n+1)
+    def apply(self, X):
+        res = self.theta[0]
+        for i in range(len(X)):
+            res = self.theta[i+1] * X[i] + res
+        return 1 / (1 + sp.exp(-res))
+    def error(self, X, Y):
+        """ Метод, возвращающий теоретический результат 
+        по переданным значениям факторов """
+        return sum((self.apply(X) - Y)**2) / (2 * len(Y))
+    def gradient_descent(self, X, Y, n_steps=1000, test=False, 
+                         x_test=None, y_test=None):
+        """ Метод, реализующий градиентный спуск """
+        i = 0
+        m = len(Y)
+        alpha = 0.3
+        steps = []
+        errors = []
+        errors_test = []
+        while(i < n_steps):
+            y_ = self.apply(X)
+    
+            self.theta[0] = self.theta[0] - alpha * sum(y_ - Y) / m
+            
+            for j in range(len(X)):
+                self.theta[j+1] = self.theta[j+1] - alpha * sum((y_ - Y)*X[j]) / m
+            
+            J = self.error(X, Y)
+            
+            steps.append(i)
+            errors.append(J)
+            
+            if test:
+                J_test = self.error(x_test, y_test)
+                errors_test.append(J_test)
+            
+            i += 1
+        if test:
+            return (steps, errors, errors_test)
+        else:
+            return (steps, errors)
+            
+
+""" Заводим и обучаем нашу регрессию """
+hyp = hypothesis2(n=7)
+
+J = hyp.error(X, Y)
+print("Initial error:", J)
+
+(steps, errors) = hyp.gradient_descent(X, Y)
+#plt.plot(x, y_, color="green")
+#plt.show()
+
+print("Final error:  ", errors[-1])
+
+plt.plot(steps, errors)
+plt.show()
+
+
+""" Пробуем обучить регрессию на тренировочной выборке из данных """
+  
+def train_test_split(length, frac=0.9):
+    split_idx = int(frac * length)
+    shuffled = sp.random.permutation(list(range(length)))
+    train = sorted(shuffled[:split_idx])
+    test = sorted(shuffled[split_idx:])
+    return (train, test)
+
+hyp2 = hypothesis2(n=7)
+
+(train, test) = train_test_split(len(Y), frac=0.8)
+
+J = hyp2.error(X[:, train], Y[train])
+print("Initial train error:", J)
+J = hyp2.error(X[:, test], Y[test])
+print("Initial test error:", J)
+
+(steps, errors, errors_test) = hyp2.gradient_descent(X[:, train], Y[train], 
+                                                    test=True, n_steps=2000,
+                                                    x_test=X[:, test], y_test=Y[test])
+print("Final train error:  ", errors[-1])
+J = hyp2.error(X[:, test], Y[test])
+print("Final test error:", J)
+plt.plot(steps, errors)
+plt.plot(steps, errors_test, color="purple")
+plt.show()
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