示例#1
0
test_character_list = chf.load_characters_kaggle_format(character_file_directory +\
 character_test_file_name, 'train', (0,100))

for i in range(train_character_list.shape[0]):
    char = train_character_list[i]
    char.calculate_char_features()

for i in range(test_character_list.shape[0]):
    if i % 1000 == 0:
        print("calculating features for " + str(i) + "th test event...")
    test_character_list[i].calculate_char_features()

print('begin softmax regression...')

Lambda = 0.05
sr.softmax_regression(train_character_list, test_character_list, Lambda)

correct_classifications = np.zeros(ch.character.total_classifications)
total_instances = np.zeros(ch.character.total_classifications)

for i in range(test_character_list.shape[0]):
    char = test_character_list[i]

    if char._predicted_classification == char._classification:
        correct_classifications[int(
            char._classification)] = correct_classifications[int(
                char._classification)] + 1

    total_instances[int(
        char._classification)] = total_instances[int(char._classification)] + 1

for i in range(train_character_list.shape[0]):
	char = train_character_list[i]
	char.calculate_char_features()


for i in range(test_character_list.shape[0]):
	if i%1000 == 0:
		print("calculating features for " + str(i) + "th test event...")
	test_character_list[i].calculate_char_features()

print('begin softmax regression...')

Lambda = 0.05
sr.softmax_regression(train_character_list, test_character_list, Lambda)


correct_classifications = np.zeros(ch.character.total_classifications)
total_instances = np.zeros(ch.character.total_classifications)

for i in range(test_character_list.shape[0]):
	char = test_character_list[i]
	
	if char._predicted_classification == char._classification:
		correct_classifications[int(char._classification)] = correct_classifications[int(char._classification)] + 1

	total_instances[int(char._classification)] = total_instances[int(char._classification)] + 1

for i in range(0, ch.character.total_classifications):
	print('i = ' + str(i) + ': ' + str(correct_classifications[i]) + "/" + str(total_instances[i]))
示例#3
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                num_correct += 1

    return float(num_correct) / groundTruth.shape[0]
    # percent total accuracy


if __name__ == "__main__":
    args = parser.parse_args()
    trainMatrix = datapath + args.trainMatrix
    trainLabels = datapath + args.trainLabels
    testMatrix = datapath + args.testMatrix
    testLabels = datapath + args.testLabels
    num_classes = args.num_classes

    # load training data
    X = np.load(trainMatrix)
    y = np.load(trainLabels)

    # train softmax regression
    Theta = softmax.softmax_regression(
        X, y, num_classes, args.reg_param, args.max_iters, args.alpha, args.converg_tol, args.verbose
    )

    # load test data
    testMatrix = np.load(testMatrix)
    testMatrix = np.append(np.ones((testMatrix.shape[0], 1)), testMatrix, 1)  # append column of 1s for intercept term
    testLabels = np.load(testLabels)

    predictions = make_predictions(Theta, testMatrix)
    print "Accuracy is ", evaluate_predictions(predictions, testLabels, num_classes)
示例#4
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                num_correct += 1

    return float(num_correct) / groundTruth.shape[0]
    #percent total accuracy


if __name__ == "__main__":
    args = parser.parse_args()
    trainMatrix = datapath + args.trainMatrix
    trainLabels = datapath + args.trainLabels
    testMatrix = datapath + args.testMatrix
    testLabels = datapath + args.testLabels
    num_classes = args.num_classes

    #load training data
    X = np.load(trainMatrix)
    y = np.load(trainLabels)

    #train softmax regression
    Theta = softmax.softmax_regression (X, y, num_classes, args.reg_param, args.max_iters, \
                      args.alpha, args.converg_tol, args.verbose)

    #load test data
    testMatrix = np.load(testMatrix)
    testMatrix = np.append(np.ones((testMatrix.shape[0], 1)), testMatrix,
                           1)  #append column of 1s for intercept term
    testLabels = np.load(testLabels)

    predictions = make_predictions(Theta, testMatrix)
    print "Accuracy is ", evaluate_predictions(predictions, testLabels,
                                               num_classes)
示例#5
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def main():
    input = input_pipeline()
    print("ready input pipeline")
    net = softmax_regression()
    _solver = solver(net, input, './log')
    _solver.train_and_test()