PythonMachineLearningExamples/p330_dendrogram.py at master · rrlyman/PythonMachineLearningExamples · GitHub

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'''

Created on Jul 21, 2016

A dendrogram is a diagram that shows the cluster composition of a dataset by

by showing cluster as levels in a tree. At each node, the cluster is

shown as subclusters. This is somewhat like the separation of the data by

a Decision Tree.

from Python Machine Learning by Sebastian Raschka under the following license

The MIT License (MIT)

Permission is hereby granted, free of charge, to any person obtaining a copy

of this software and associated documentation files (the "Software"), to deal

in the Software without restriction, including without limitation the rights

to use, copy, modify, merge, publish, distribute, sublicense, and/or sell

copies of the Software, and to permit persons to whom the Software is

furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all

copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR

IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,

FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE

AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER

LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,

OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE

SOFTWARE.

@author: richard lyman

'''

import ocr_utils

import matplotlib.pyplot as plt

##############################################

# separate the original images by cluster

# print(km.cluster_centers_.shape)

n=300

variables = ['X', 'Y', 'Z']

labels = ['ID_0','ID_1','ID_2','ID_3','ID_4']

chars_to_train = range(48,51)

columnsXY=(9,17)

column_str = 'column_sum{}'.format(list(columnsXY))

input_filters_dict = {'m_label': chars_to_train, 'font': 'E13B'}

# output the character label and the image and column sums

output_feature_list = ['m_label','image', column_str]

# read the complete image (20x20) = 400 pixels for each character

ds = ocr_utils.read_data(input_filters_dict=input_filters_dict,

output_feature_list=output_feature_list,

random_state=0)

y = ds.train.features[0][:n]

X_image = ds.train.features[1][:n]

X = ds.train.features[2][:n]

from scipy.spatial.distance import pdist

row_dist = pdist(X, metric='euclidean')

print(row_dist)

from scipy.cluster.hierarchy import linkage

#method 1 using a condensed matrix

row_clusters = linkage(row_dist, method='complete', metric='euclidean')

print (row_clusters)

#method 2 using raw data

row_clusters = linkage(X, method='complete', metric='euclidean')

print()

print (row_clusters)

from scipy.cluster.hierarchy import dendrogram

# make dendrogram black (part 1/2)

# from scipy.cluster.hierarchy import set_link_color_palette

# set_link_color_palette(['black'])

row_dendr = dendrogram(row_clusters, p=12, truncate_mode = 'lastp')

plt.tight_layout()

plt.ylabel('Euclidean distance')

#plt.savefig('./figures/dendrogram.png', dpi=300,

# bbox_inches='tight')

title = "Dendogram"

plt.title(title)

ocr_utils.show_figures(plt, title)

print ('\n########################### No Errors ####################################')

PythonMachineLearningExamples/p330_dendrogram.py at master · rrlyman/PythonMachineLearningExamples · GitHub

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