本文整理汇总了Python中Features.getMatrix方法的典型用法代码示例。如果您正苦于以下问题：Python Features.getMatrix方法的具体用法？Python Features.getMatrix怎么用？Python Features.getMatrix使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类Features的用法示例。
在下文中一共展示了Features.getMatrix方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: main
# 需要导入模块: import Features [as 别名]
# 或者: from Features import getMatrix [as 别名]
def main():
arguments = validateInput(sys.argv)
maxIterations, regularization, stepSize, lmbd, featureSet = arguments
print maxIterations, regularization, stepSize, lmbd, featureSet
trainData = readFile('train.csv')
validationData = readFile('validation.csv')
testData = readFile('test.csv')
trainSize = trainData.shape[0]
validationSize = validationData.shape[0]
testSize = testData.shape[0]
print "Number of training examples: " + str(trainSize)
# Extract Features
Features.extractFeatures(trainData[:,0], featureSet)
print "Extracted Features:"
if featureSet == 1 or featureSet == 3:
print "Unigram: " + str(Features.getLength(1))
if featureSet == 2 or featureSet == 3:
print "Bigram: " + str(Features.getLength(2))
# Construct Input Matrices X
xTrain = Features.getMatrix(trainData[:,0], featureSet)
print "Train Matrix built"
xValidation = Features.getMatrix(validationData[:,0], featureSet)
print "Validation Matrix built"
xTest = Features.getMatrix(testData[:,0], featureSet)
print "Test Matrix built"
yTrain = extractLabel(trainData[:,1])
yVailidation = extractLabel(validationData[:,1])
yTest = extractLabel(testData[:,1])
# Train the model
theta = GD(xTrain, yTrain, trainSize, maxIterations, regularization, stepSize, lmbd, featureSet)
print "Final Theta: " + str(theta)
# Classify
trainResult = predict(xTrain, trainSize, theta, featureSet)
print "Train Result: " + str(trainResult)
validationResult = predict(xValidation, validationSize, theta, featureSet)
print "Validation Result: " + str(validationResult)
testResult = predict(xTest, testSize, theta, featureSet)
print "Test Result: " + str(testResult)
# Performance
print "\nPerformance on training data:"
performance(trainResult, trainData[:,1])
print "\nPerformance on validation data:"
performance(validationResult, validationData[:,1])
print "\nPerformance on test data:"
performance(testResult, testData[:,1])