86 lines
2.1 KiB
Python
86 lines
2.1 KiB
Python
# Code from Chapter 3 of Machine Learning: An Algorithmic Perspective (2nd Edition)
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# by Stephen Marsland (http://stephenmonika.net)
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# You are free to use, change, or redistribute the code in any way you wish for
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# non-commercial purposes, but please maintain the name of the original author.
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# This code comes with no warranty of any kind.
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# Stephen Marsland, 2008, 2014
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import numpy as np
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class pcn:
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""" A basic Perceptron"""
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def __init__(self,inputs,targets):
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""" Constructor """
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# Set up network size
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if np.ndim(inputs)>1:
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self.nIn = np.shape(inputs)[1]
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else:
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self.nIn = 1
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if np.ndim(targets)>1:
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self.nOut = np.shape(targets)[1]
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else:
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self.nOut = 1
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self.nData = np.shape(inputs)[0]
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# Initialise network
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self.weights = np.random.rand(self.nIn+1,self.nOut)*0.1-0.05
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def pcntrain(self,inputs,targets,eta,nIterations):
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""" Train the thing """
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# Add the inputs that match the bias node
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inputs = np.concatenate((inputs,-np.ones((self.nData,1))),axis=1)
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# Training
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change = range(self.nData)
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for n in range(nIterations):
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self.activations = self.pcnfwd(inputs);
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self.weights -= eta*np.dot(np.transpose(inputs),self.activations-targets)
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# Randomise order of inputs
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#np.random.shuffle(change)
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#inputs = inputs[change,:]
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#targets = targets[change,:]
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#return self.weights
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def pcnfwd(self,inputs):
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""" Run the network forward """
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# Compute activations
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activations = np.dot(inputs,self.weights)
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# Threshold the activations
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return np.where(activations>0,1,0)
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def confmat(self,inputs,targets):
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"""Confusion matrix"""
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# Add the inputs that match the bias node
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inputs = np.concatenate((inputs,-np.ones((self.nData,1))),axis=1)
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outputs = np.dot(inputs,self.weights)
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nClasses = np.shape(targets)[1]
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if nClasses==1:
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nClasses = 2
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outputs = np.where(outputs>0,1,0)
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else:
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# 1-of-N encoding
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outputs = np.argmax(outputs,1)
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targets = np.argmax(targets,1)
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cm = np.zeros((nClasses,nClasses))
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for i in range(nClasses):
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for j in range(nClasses):
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cm[i,j] = np.sum(np.where(outputs==i,1,0)*np.where(targets==j,1,0))
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print cm
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print np.trace(cm)/np.sum(cm)
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