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Author: MainakRepositor

generate_data.py

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+#!/usr/bin/env python
+#coding: utf-8
+
+import numpy as np
+
+def generateData(n):
+    """
+    generates a 2D linearly separable dataset with n samples.
+    The thired element of the sample is the label
+    """
+    xb = (np.random.rand(n) * 2 -1) / 2 - 0.5
+    yb = (np.random.rand(n) * 2 -1) / 2 + 0.5
+    xr = (np.random.rand(n) * 2 -1) / 2 + 0.5
+    yr = (np.random.rand(n) * 2 -1) / 2 - 0.5
+    inputs = []
+    inputs.extend([[xb[i], yb[i], 1] for i in xrange(n)])
+    inputs.extend([[xr[i], yr[i], -1] for i in xrange(n)])
+    return inputs

perceptron.py

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+#!/usr/bin/env python
+#coding: utf-8
+
+import random
+
+class Perceptron(object):
+    """docstring for Perceptron"""
+    def __init__(self):
+        super(Perceptron, self).__init__()
+        self.w = [random.random() * 2 - 1 for _ in xrange(2)] # weights
+        self.learningRate = 0.1
+
+    def response(self, x):
+        """perceptron output"""
+        # y = x[0] * self.w[0] + x[1] * self.w[1] # dot product between w and x
+        y = sum([i * j for i, j in zip(self.w, x)]) # more pythonic
+        if y >= 0:
+            return 1
+        else:
+            return -1
+
+    def updateWeights(self, x, iterError):
+        """
+        upates the wights status, w at time t+1 is
+        w(t+1) = w(t) + learningRate * (d - r) * x
+        iterError is (d - r)
+        """
+        # self.w[0] += self.learningRate * iterError * x[0]
+        # self.w[1] += self.learningRate * iterError * x[1]
+        self.w = \
+            [i + self.learningRate * iterError * j for i, j in zip(self.w, x)]
+
+    def train(self, data):
+        """
+        trains all the vector in data.
+        Every vector in data must three elements.
+        the third eclemnt(x[2]) must be the label(desired output)
+        """
+        learned = False
+        iteration = 0
+        while not learned:
+            globalError = 0.0
+            for x in data: # for each sample
+                r = self.response(x)
+                if x[2] != r: # if have a wrong response
+                    iterError = x[2] - r # desired response - actual response
+                    self.updateWeights(x, iterError)
+                    globalError += abs(iterError)
+            iteration += 1
+            if globalError == 0.0 or iteration >= 100: # stop criteria
+                print 'iterations: %s' % iteration
+                learned = True # stop learing

test.py

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+#!/usr/bin/env python
+#coding: utf-8
+
+from matplotlib.pylab import *
+# local module
+import generate_data
+import perceptron
+
+trainset = generate_data.generateData(80) # train set generation
+testset = generate_data.generateData(20) # test set generation
+p = perceptron.Perceptron() # use a short
+p.train(trainset)
+
+#Perceptron test
+for x in testset:
+    r = p.response(x)
+    if r != x[2]: # if the response is not correct
+        print 'not hit.'
+    if r == 1:
+        plot(x[0], x[1], 'ob')
+    else:
+        plot(x[0], x[1], 'or')
+
+# plot of the separation line. 
+# The centor of line is the coordinate origin
+# So the length of line is 2
+# The separation line is orthogonal to w
+n = norm(p.w) # aka the length of p.w vector
+ww = p.w / n # a unit vector
+ww1 = [ww[1], -ww[0]]
+ww2 = [-ww[1], ww[0]]
+plot([ww1[0], ww2[0]], [ww1[1], ww2[1]], '--k')
+show()