p330_dendrogram.py

'''
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)

Copyright (c) 2015, 2016 SEBASTIAN RASCHKA (mail@sebastianraschka.com)

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 ####################################')