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#!/usr/bin/python

''' random_forest.py

The random forest uses weak learners to build a strong learner

A random subset of samples is drawn and then at each node a decision

tree is grown from a smaller subset of those bootstrap samples

This is repeated a number of times and then the decision trees are

combined via majority vote.

1) run the random forest on the e13b data

2) plot the decision regions

Created on Jun 23, 2016

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 numpy as np

import ocr_utils

from sklearn.preprocessing import StandardScaler

if __name__ == '__main__':

y_train, X_train, y_test, X_test, labels = ocr_utils.load_E13B(chars_to_train = (48,49,50) , columns=(9,17), test_size=0.3, nChars=300, random_state=0)

sc = StandardScaler()

sc.fit(X_train)

X_train_std = sc.transform(X_train)

X_test_std = sc.transform(X_test)

X_combined_std = np.vstack((X_train_std, X_test_std))

y_combined = np.hstack((y_train, y_test))

X_combined = np.vstack((X_train, X_test))

y_combined = np.hstack((y_train, y_test))

from sklearn.ensemble import RandomForestClassifier

forest = RandomForestClassifier(criterion='entropy',

n_estimators=10,

random_state=1,

n_jobs=2)

forest.fit(X_train, y_train)

ocr_utils.plot_decision_regions(X=X_combined,

y=y_combined,

classifier=forest,

labels=labels,

test_idx=range(len(X_test_std),len(X_combined_std)),

title='random_forest')

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