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number_recognition.py
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number_recognition.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
'''
File: number_recognition.py
Author: Xiang Ji
Email: xj4hm@virginia.edu
Date: November, 2015
Brief: Homework 3 OCR
Usage: python number_recognition.py model trainData testData
'''
import sys
import os
import numpy as np
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.linear_model import Perceptron
from sklearn.svm import SVC
from sklearn.decomposition import RandomizedPCA
def loadData(file):
data = np.loadtxt(file)
number = data[:,0]
pixels = data[:, range(1,256)]
return number, pixels
def decision_tree(train, test):
y = []
trainY, trainX = loadData(train)
testY, testX = loadData(test)
clf = DecisionTreeClassifier(criterion = "entropy", splitter = "best", random_state = 0)
clf.fit(trainX, trainY)
y = clf.predict(testX)
error = 1 - clf.score(testX, testY)
print 'Test error: ' + str(error)
return y
def knn(train, test):
y = []
trainY, trainX = loadData(train)
testY, testX = loadData(test)
neigh = KNeighborsClassifier(n_neighbors = 4, weights = 'distance')
neigh.fit(trainX, trainY)
y = neigh.predict(testX)
testError = 1 - neigh.score(testX, testY)
trainError = 1 - neigh.score(trainX, trainY)
print 'Test error: ' + str(testError)
print 'Training error: ' + str(trainError)
return y
def neural_net(train, test):
y = []
trainY, trainX = loadData(train)
testY, testX = loadData(test)
neuralNet = Perceptron()
neuralNet.fit(trainX, trainY)
y = neuralNet.predict(testX)
testError = 1 - neuralNet.score(testX, testY)
print 'Test error: ' + str(testError)
return y
def svm(train, test):
y = []
trainY, trainX = loadData(train)
testY, testX = loadData(test)
svmClassifier = SVC(kernel = 'poly')
svmClassifier.fit(trainX, trainY)
y = svmClassifier.predict(testX)
testError = 1 - svmClassifier.score(testX, testY)
print 'Test error: ' + str(testError)
return y
def pca_knn(train, test):
y = []
trainY, trainX = loadData(train)
testY, testX = loadData(test)
PCA = RandomizedPCA(n_components = 64)
#n_components = dim
PCA.fit(trainX)
reducedTrainX = PCA.transform(trainX)
reducedTestX = PCA.transform(testX)
neigh = KNeighborsClassifier(n_neighbors = 4, weights = 'distance')
neigh.fit(reducedTrainX, trainY)
y = neigh.predict(reducedTestX)
testError = 1 - neigh.score(reducedTestX, testY)
print 'Test error: ' + str(testError)
return y
def pca_svm(train, test):
y = []
trainY, trainX = loadData(train)
testY, testX = loadData(test)
PCA = RandomizedPCA(n_components = 64)
PCA.fit(trainX)
reducedTrainX = PCA.transform(trainX)
reducedTestX = PCA.transform(testX)
svmClassifier = SVC(kernel = 'poly')
svmClassifier.fit(reducedTrainX, trainY)
y = svmClassifier.predict(reducedTestX)
testError = 1 - svmClassifier.score(reducedTestX, testY)
print 'Test error: ' + str(testError)
return y
if __name__ == '__main__':
model = sys.argv[1]
train = sys.argv[2]
test = sys.argv[3]
if model == "dtree":
print(decision_tree(train, test))
elif model == "knn":
print(knn(train, test))
elif model == "net":
print(neural_net(train, test))
elif model == "svm":
print(svm(train, test))
elif model == "pcaknn":
print(pca_knn(train, test))
elif model == "pcasvm":
print(pca_svm(train, test))
else:
print("Invalid method selected!")