Python visualize Examples

Example #1

File: alg_application3.py Project: Yueleng/IIPP

def question5(filename):
    data = 'unifiedCancerData_111.csv'
    dist = distortion(
        visualize(data, filename, lambda x: hierarchical_clustering(x, 9)),
        load_data_table(data))
    print('Distortion in question5, hierarchical_clustering = %f (%s)' %
          (dist, dist))

Example #2

File: alg_application3.py Project: Yueleng/IIPP

def question6(filename):
    data = 'unifiedCancerData_111.csv'
    dist = distortion(
        visualize(data, filename, lambda x: kmeans_clustering(x, 9, 5)),
        load_data_table(data))
    print('Distortion in question6, kmeans = %f (%s)' % (dist, dist))

Example #3

File: alg_application3.py Project: Yueleng/IIPP

def question3(filename):
    visualize('unifiedCancerData_3108.csv', filename,
              lambda x: kmeans_clustering(x, 15, 5))

Example #4

File: alg_application3.py Project: Yueleng/IIPP

def question2(filename):
    visualize('unifiedCancerData_3108.csv', filename,
              lambda x: hierarchical_clustering(x, 15))

Example #5

File: application3.py Project: balta2ar/algorithmicthink-001

def question6(filename):
    data = 'data/unifiedCancerData_111.csv'
    dist = distortion(visualize(data, filename,
                                lambda x: kmeans_clustering(x, 9, 5)),
                      load_data_table(data))
    print('Distortion in question6, kmeans = %f (%s)' % (dist, dist))

Example #6

File: application3.py Project: balta2ar/algorithmicthink-001

def question5(filename):
    data = 'data/unifiedCancerData_111.csv'
    dist = distortion(visualize(data, filename,
                                lambda x: hierarchical_clustering(x, 9)),
                      load_data_table(data))
    print('Distortion in question5, hierarchical_clustering = %f (%s)' % (dist, dist))

Example #7

File: application3.py Project: balta2ar/algorithmicthink-001

def question3(filename):
    visualize('data/unifiedCancerData_3108.csv', filename,
              lambda x: kmeans_clustering(x, 15, 5))

Example #8

File: application3.py Project: balta2ar/algorithmicthink-001

def question2(filename):
    visualize('data/unifiedCancerData_3108.csv', filename,
              lambda x: hierarchical_clustering(x, 15))