Python get_stockset示例

示例#1

def main():
    test_dataset = mock_Chinese_stock_price.get_stockset()
    v1 = test_dataset[0]['input']
    v2 = test_dataset[1]['input']
    print v1
    print v2
    print euclidean(v1, v2)

示例#2

文件： cross_validation.py 项目： hanhanwu/Hanhan-Machine-Learning-Model-Implementation

def main():
    data_set = mock_Chinese_stock_price.get_stockset()
    cv_total_error_unweighted = cross_validate(data_set, algr = KNN.get_KNN, trails=200)
    cv_total_error_weighted = cross_validate(data_set, algr = KNN.get_weightedKNN, trails=200)
    
    print 'cross validation, using un-weighted KNN: ', cv_total_error_unweighted
    print 'cross validation, using weighted KNN: ', cv_total_error_weighted

示例#3

文件： euclidean_distance.py 项目： hanhanwu/Hanhan-Machine-Learning-Model-Implementation

def main():
    test_dataset = mock_Chinese_stock_price.get_stockset()
    v1 = test_dataset[0]['input']
    v2 = test_dataset[1]['input']
    print v1
    print v2
    print euclidean(v1, v2)

示例#4

文件： probability_guess.py 项目： hanhanwu/Hanhan-Machine-Learning-Model-Implementation

def main():
    data = mock_Chinese_stock_price.get_stockset()
    print prob_guess(data, (9, 2, 12), 10, 100)
    print prob_guess(data, (3, 2, 5), 10, 100)
    print prob_guess(data, (3, 2, 5), 1, 10)
    
    # plot using accumulative probability
    accumulative_plot(data, (9, 2, 12), 100)
    
    # plot using probabilities graph
    probabilitygraph(data, (9, 2, 12), 100)

示例#5

def main():
    data = mock_Chinese_stock_price.get_stockset()
    print prob_guess(data, (9, 2, 12), 10, 100)
    print prob_guess(data, (3, 2, 5), 10, 100)
    print prob_guess(data, (3, 2, 5), 1, 10)

    # plot using accumulative probability
    accumulative_plot(data, (9, 2, 12), 100)

    # plot using probabilities graph
    probabilitygraph(data, (9, 2, 12), 100)

示例#6

文件： cross_validation.py 项目： generlist/Hanhan-Machine-Learning-Model-Implementation

def main():
    data_set = mock_Chinese_stock_price.get_stockset()
    cv_total_error_unweighted = cross_validate(data_set,
                                               algr=KNN.get_KNN,
                                               trails=200)
    cv_total_error_weighted = cross_validate(data_set,
                                             algr=KNN.get_weightedKNN,
                                             trails=200)

    print 'cross validation, using un-weighted KNN: ', cv_total_error_unweighted
    print 'cross validation, using weighted KNN: ', cv_total_error_weighted

示例#7

def main():
    training_data = mock_Chinese_stock_price.get_stockset()

    # KNN without weight
    print 'using un-weighted KNN'
    print get_KNN(training_data, (9, 2, 12), k=3)
    print get_KNN(training_data, (5, 3, 7), k=3)

    # KNN with weight
    print 'weighted KNN using Gaussian function'
    print 'Gaussian Weight Function: ', get_weightedKNN(
        training_data, (9, 2, 12))
    print 'Inverse Weight Function: ', get_weightedKNN(training_data,
                                                       (9, 2, 12),
                                                       weight_f=inverse_weight)
    print 'Subtract Weight Function: ', get_weightedKNN(
        training_data, (9, 2, 12), weight_f=subtract_weight)