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SVM_nitin.py
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#Author: Nitin A Jain
import numpy as np
from sklearn.svm import SVC,LinearSVC
import common
def LinearSVC_onData():
#Parsing Full training dataset
XFull = common.parseFile('../UCI HAR Dataset/train/X_train.txt')
YFull = common.parseFile('../UCI HAR Dataset/train/y_train.txt')
#Parsing Full testing dataset
XFullTest = common.parseFile('../UCI HAR Dataset/test/X_test.txt')
YFullTest = common.parseFile('../UCI HAR Dataset/test/y_test.txt')
#################################################################################################################################
#Getting the dataset associated with Dynamic Activities on training
X_Dynamic,Y_Dynamic = common.getDataSubset(XFull,YFull.flatten(),[1,2,3,4,5,6])
#Getting the dataset associated with Dynamic Activities on testing
X_DynamicTest,Y_DynamicTest = common.getDataSubset(XFullTest,YFullTest.flatten(),[1,2,3,4,5,6])
#Fitting data using LinearSVC classifier
clf = LinearSVC(multi_class='crammer_singer')
clf.fit(X_Dynamic, Y_Dynamic.flatten())
precision,recall,fscore = common.checkAccuracy(clf.predict(X_Dynamic),Y_Dynamic,[1,2,3,4,5,6])
print common.createConfusionMatrix(clf.predict(X_Dynamic).flatten(),Y_Dynamic.flatten(),[1,2,3,4,5,6])
#Fitting data using LinearSVC classifier
clf = SVC(kernel = "linear")
clf.fit(X_Dynamic, Y_Dynamic.flatten())
precision,recall,fscore = common.checkAccuracy(clf.predict(X_Dynamic),Y_Dynamic,[1,2,3,4,5,6])
print common.createConfusionMatrix(clf.predict(X_Dynamic).flatten(),Y_Dynamic.flatten(),[1,2,3,4,5,6])
precision,recall,fscore = common.checkAccuracy(clf.predict(X_DynamicTest),Y_DynamicTest,[1,2,3,4,5,6])
print common.createConfusionMatrix(clf.predict(X_DynamicTest).flatten(),Y_DynamicTest.flatten(),[1,2,3,4,5,6])
print fscore
#################################################################################################################################
#Getting the dataset associated with Dynamic Activities on training
X_Dynamic,Y_Dynamic = common.getDataSubset(XFull,YFull.flatten(),[1,2,3,4,5,6])
#Getting the dataset associated with Dynamic Activities on testing
X_DynamicTest,Y_DynamicTest = common.getDataSubset(XFullTest,YFullTest.flatten(),[1,2,3,4,5,6])
X_Dynamic = common.getPowerK(X_Dynamic,[1,2])
X_DynamicTest = common.getPowerK(X_DynamicTest,[1,2])
#Fitting data using LinearSVC classifier
clf = SVC(kernel = "linear")
clf.fit(X_Dynamic, Y_Dynamic.flatten())
precision,recall,fscore = common.checkAccuracy(clf.predict(X_DynamicTest),Y_DynamicTest,[1,2,3,4,5,6])
print common.createConfusionMatrix(clf.predict(X_DynamicTest).flatten(),Y_DynamicTest.flatten(),[1,2,3,4,5,6])
print fscore
#################################################################################################################################
#Getting the dataset associated with Dynamic Activities on training
X_Dynamic,Y_Dynamic = common.getDataSubset(XFull,YFull.flatten(),[1,2,3,4,5,6])
#Getting the dataset associated with Dynamic Activities on testing
X_DynamicTest,Y_DynamicTest = common.getDataSubset(XFullTest,YFullTest.flatten(),[1,2,3,4,5,6])
X_Dynamic = common.getPowerK(X_Dynamic,[1,2,3])
X_DynamicTest = common.getPowerK(X_DynamicTest,[1,2,3])
#Fitting data using LinearSVC classifier
clf = SVC(kernel = "linear")
clf.fit(X_Dynamic, Y_Dynamic.flatten())
precision,recall,fscore = common.checkAccuracy(clf.predict(X_DynamicTest),Y_DynamicTest,[1,2,3,4,5,6])
print common.createConfusionMatrix(clf.predict(X_DynamicTest).flatten(),Y_DynamicTest.flatten(),[1,2,3,4,5,6])
print fscore
#################################################################################################################################
#Getting the dataset associated with Dynamic Activities on training
X_Dynamic,Y_Dynamic = common.getDataSubset(XFull,YFull.flatten(),[1,2,3])
#Getting the dataset associated with Dynamic Activities on testing
X_DynamicTest,Y_DynamicTest = common.getDataSubset(XFullTest,YFullTest.flatten(),[1,2,3])
print len(X_DynamicTest),len(Y_DynamicTest)
#Fitting data using LinearSVC classifier
clf = SVC(kernel = "linear")
clf.fit(X_Dynamic, Y_Dynamic.flatten())
precision,recall,fscore = common.checkAccuracy(clf.predict(X_DynamicTest),Y_DynamicTest,[1,2,3])
print common.createConfusionMatrix(clf.predict(X_DynamicTest).flatten(),Y_DynamicTest.flatten(),[1,2,3])
print fscore
#################################################################################################################################
#Getting the dataset associated with Dynamic Activities on training
X_Dynamic,Y_Dynamic = common.getDataSubset(XFull,YFull.flatten(),[1,2,3])
#Getting the dataset associated with Dynamic Activities on testing
X_DynamicTest,Y_DynamicTest = common.getDataSubset(XFullTest,YFullTest.flatten(),[1,2,3])
X_Dynamic = common.getPowerK(X_Dynamic,[1,2])
X_DynamicTest = common.getPowerK(X_DynamicTest,[1,2])
#Fitting data using LinearSVC classifier
clf = SVC(kernel = "linear")
clf.fit(X_Dynamic, Y_Dynamic.flatten())
precision,recall,fscore = common.checkAccuracy(clf.predict(X_DynamicTest),Y_DynamicTest,[1,2,3])
print common.createConfusionMatrix(clf.predict(X_DynamicTest).flatten(),Y_DynamicTest.flatten(),[1,2,3])
print fscore
#################################################################################################################################
#Getting the dataset associated with Dynamic Activities on training
X_Dynamic,Y_Dynamic = common.getDataSubset(XFull,YFull.flatten(),[1,2,3])
#Getting the dataset associated with Dynamic Activities on testing
X_DynamicTest,Y_DynamicTest = common.getDataSubset(XFullTest,YFullTest.flatten(),[1,2,3])
X_Dynamic = common.getPowerK(X_Dynamic,[1,2,3])
X_DynamicTest = common.getPowerK(X_DynamicTest,[1,2,3])
#Fitting data using LinearSVC classifier
clf = SVC(kernel = "linear")
clf.fit(X_Dynamic, Y_Dynamic.flatten())
precision,recall,fscore = common.checkAccuracy(clf.predict(X_DynamicTest),Y_DynamicTest,[1,2,3])
print common.createConfusionMatrix(clf.predict(X_DynamicTest).flatten(),Y_DynamicTest.flatten(),[1,2,3])
print fscore
#################################################################################################################################
#Getting the dataset associated with Non-Dynamic Activities on training
X_NonDynamic,Y_NonDynamic = common.getDataSubset(XFull,YFull.flatten(),[4,5,6])
#Getting the dataset associated with Non-Dynamic Activities on testing
X_NonDynamicTest,Y_NonDynamicTest = common.getDataSubset(XFullTest,YFullTest.flatten(),[4,5,6])
#Fitting data using LinearSVC classifier
clf = SVC(kernel = "linear")
clf.fit(X_NonDynamic, Y_NonDynamic.flatten())
precision,recall,fscore = common.checkAccuracy(clf.predict(X_NonDynamicTest),Y_NonDynamicTest,[4,5,6])
print common.createConfusionMatrix(clf.predict(X_NonDynamicTest).flatten(),Y_NonDynamicTest.flatten(),[4,5,6])
print fscore
#################################################################################################################################
#Getting the dataset associated with Dynamic Activities on training
X_Dynamic,Y_Dynamic = common.getDataSubset(XFull,YFull.flatten(),[4,5,6])
#Getting the dataset associated with Dynamic Activities on testing
X_DynamicTest,Y_DynamicTest = common.getDataSubset(XFullTest,YFullTest.flatten(),[4,5,6])
X_Dynamic = common.getPowerK(X_Dynamic,[1,2])
X_DynamicTest = common.getPowerK(X_DynamicTest,[1,2])
#Fitting data using LinearSVC classifier
clf = SVC(kernel = "linear")
clf.fit(X_Dynamic, Y_Dynamic.flatten())
precision,recall,fscore = common.checkAccuracy(clf.predict(X_DynamicTest),Y_DynamicTest,[4,5,6])
print common.createConfusionMatrix(clf.predict(X_DynamicTest).flatten(),Y_DynamicTest.flatten(),[4,5,6])
print fscore
#################################################################################################################################
#Getting the dataset associated with Dynamic Activities on training
X_Dynamic,Y_Dynamic = common.getDataSubset(XFull,YFull.flatten(),[4,5,6])
#Getting the dataset associated with Dynamic Activities on testing
X_DynamicTest,Y_DynamicTest = common.getDataSubset(XFullTest,YFullTest.flatten(),[4,5,6])
X_Dynamic = common.getPowerK(X_Dynamic,[1,2,3])
X_DynamicTest = common.getPowerK(X_DynamicTest,[1,2,3])
#Fitting data using LinearSVC classifier
clf = SVC(kernel = "linear")
clf.fit(X_Dynamic, Y_Dynamic.flatten())
precision,recall,fscore = common.checkAccuracy(clf.predict(X_DynamicTest),Y_DynamicTest,[4,5,6])
print common.createConfusionMatrix(clf.predict(X_DynamicTest).flatten(),Y_DynamicTest.flatten(),[4,5,6])
print fscore
#################################################################################################################################
def LinearSVC_onAccData():
XFull = common.parseFile('../UCI HAR Dataset/train/X_train.txt')
YFull = common.parseFile('../UCI HAR Dataset/train/y_train.txt')
XFull = common.getAccFeatures(XFull)
#Parsing Full testing dataset
XFullTest = common.parseFile('../UCI HAR Dataset/test/X_test.txt')
YFullTest = common.parseFile('../UCI HAR Dataset/test/y_test.txt')
XFullTest = common.getAccFeatures(XFullTest)
#################################################################################################################################
#Getting the dataset associated with Dynamic Activities on training
X_Dynamic,Y_Dynamic = common.getDataSubset(XFull,YFull.flatten(),[1,2,3,4,5,6])
#Getting the dataset associated with Dynamic Activities on testing
X_DynamicTest,Y_DynamicTest = common.getDataSubset(XFullTest,YFullTest.flatten(),[1,2,3,4,5,6])
#Fitting data using LinearSVC classifier
clf = SVC(kernel = "linear")
clf.fit(X_Dynamic, Y_Dynamic.flatten())
precision,recall,fscore = common.checkAccuracy(clf.predict(X_DynamicTest),Y_DynamicTest,[1,2,3,4,5,6])
print common.createConfusionMatrix(clf.predict(X_DynamicTest).flatten(),Y_DynamicTest.flatten(),[1,2,3,4,5,6])
print fscore
#################################################################################################################################
#Getting the dataset associated with Dynamic Activities on training
X_Dynamic,Y_Dynamic = common.getDataSubset(XFull,YFull.flatten(),[1,2,3,4,5,6])
#Getting the dataset associated with Dynamic Activities on testing
X_DynamicTest,Y_DynamicTest = common.getDataSubset(XFullTest,YFullTest.flatten(),[1,2,3,4,5,6])
X_Dynamic = common.getPowerK(X_Dynamic,[1,2])
X_DynamicTest = common.getPowerK(X_DynamicTest,[1,2])
#Fitting data using LinearSVC classifier
clf = SVC(kernel = "linear")
clf.fit(X_Dynamic, Y_Dynamic.flatten())
precision,recall,fscore = common.checkAccuracy(clf.predict(X_DynamicTest),Y_DynamicTest,[1,2,3,4,5,6])
print common.createConfusionMatrix(clf.predict(X_DynamicTest).flatten(),Y_DynamicTest.flatten(),[1,2,3,4,5,6])
print fscore
#################################################################################################################################
#Getting the dataset associated with Dynamic Activities on training
X_Dynamic,Y_Dynamic = common.getDataSubset(XFull,YFull.flatten(),[1,2,3,4,5,6])
#Getting the dataset associated with Dynamic Activities on testing
X_DynamicTest,Y_DynamicTest = common.getDataSubset(XFullTest,YFullTest.flatten(),[1,2,3,4,5,6])
X_Dynamic = common.getPowerK(X_Dynamic,[1,2,3])
X_DynamicTest = common.getPowerK(X_DynamicTest,[1,2,3])
#Fitting data using LinearSVC classifier
clf = SVC(kernel = "linear")
clf.fit(X_Dynamic, Y_Dynamic.flatten())
precision,recall,fscore = common.checkAccuracy(clf.predict(X_DynamicTest),Y_DynamicTest,[1,2,3,4,5,6])
print common.createConfusionMatrix(clf.predict(X_DynamicTest).flatten(),Y_DynamicTest.flatten(),[1,2,3,4,5,6])
print fscore
#################################################################################################################################
#Getting the dataset associated with Dynamic Activities on training
X_Dynamic,Y_Dynamic = common.getDataSubset(XFull,YFull.flatten(),[1,2,3])
#Getting the dataset associated with Dynamic Activities on testing
X_DynamicTest,Y_DynamicTest = common.getDataSubset(XFullTest,YFullTest.flatten(),[1,2,3])
print len(X_DynamicTest),len(Y_DynamicTest)
#Fitting data using LinearSVC classifier
clf = SVC(kernel = "linear")
clf.fit(X_Dynamic, Y_Dynamic.flatten())
precision,recall,fscore = common.checkAccuracy(clf.predict(X_DynamicTest),Y_DynamicTest,[1,2,3])
print common.createConfusionMatrix(clf.predict(X_DynamicTest).flatten(),Y_DynamicTest.flatten(),[1,2,3])
print fscore
#################################################################################################################################
#Getting the dataset associated with Dynamic Activities on training
X_Dynamic,Y_Dynamic = common.getDataSubset(XFull,YFull.flatten(),[1,2,3])
#Getting the dataset associated with Dynamic Activities on testing
X_DynamicTest,Y_DynamicTest = common.getDataSubset(XFullTest,YFullTest.flatten(),[1,2,3])
X_Dynamic = common.getPowerK(X_Dynamic,[1,2])
X_DynamicTest = common.getPowerK(X_DynamicTest,[1,2])
#Fitting data using LinearSVC classifier
clf = SVC(kernel = "linear")
clf.fit(X_Dynamic, Y_Dynamic.flatten())
precision,recall,fscore = common.checkAccuracy(clf.predict(X_DynamicTest),Y_DynamicTest,[1,2,3])
print common.createConfusionMatrix(clf.predict(X_DynamicTest).flatten(),Y_DynamicTest.flatten(),[1,2,3])
print fscore
#################################################################################################################################
#Getting the dataset associated with Dynamic Activities on training
X_Dynamic,Y_Dynamic = common.getDataSubset(XFull,YFull.flatten(),[1,2,3])
#Getting the dataset associated with Dynamic Activities on testing
X_DynamicTest,Y_DynamicTest = common.getDataSubset(XFullTest,YFullTest.flatten(),[1,2,3])
X_Dynamic = common.getPowerK(X_Dynamic,[1,2,3])
X_DynamicTest = common.getPowerK(X_DynamicTest,[1,2,3])
#Fitting data using LinearSVC classifier
clf = SVC(kernel = "linear")
clf.fit(X_Dynamic, Y_Dynamic.flatten())
precision,recall,fscore = common.checkAccuracy(clf.predict(X_DynamicTest),Y_DynamicTest,[1,2,3])
print common.createConfusionMatrix(clf.predict(X_DynamicTest).flatten(),Y_DynamicTest.flatten(),[1,2,3])
print fscore
#################################################################################################################################
#Getting the dataset associated with Non-Dynamic Activities on training
X_NonDynamic,Y_NonDynamic = common.getDataSubset(XFull,YFull.flatten(),[4,5,6])
#Getting the dataset associated with Non-Dynamic Activities on testing
X_NonDynamicTest,Y_NonDynamicTest = common.getDataSubset(XFullTest,YFullTest.flatten(),[4,5,6])
#Fitting data using LinearSVC classifier
clf = SVC(kernel = "linear")
clf.fit(X_NonDynamic, Y_NonDynamic.flatten())
precision,recall,fscore = common.checkAccuracy(clf.predict(X_NonDynamicTest),Y_NonDynamicTest,[4,5,6])
print common.createConfusionMatrix(clf.predict(X_NonDynamicTest).flatten(),Y_NonDynamicTest.flatten(),[4,5,6])
print fscore
#################################################################################################################################
#Getting the dataset associated with Dynamic Activities on training
X_Dynamic,Y_Dynamic = common.getDataSubset(XFull,YFull.flatten(),[4,5,6])
#Getting the dataset associated with Dynamic Activities on testing
X_DynamicTest,Y_DynamicTest = common.getDataSubset(XFullTest,YFullTest.flatten(),[4,5,6])
X_Dynamic = common.getPowerK(X_Dynamic,[1,2])
X_DynamicTest = common.getPowerK(X_DynamicTest,[1,2])
#Fitting data using LinearSVC classifier
clf = SVC(kernel = "linear")
clf.fit(X_Dynamic, Y_Dynamic.flatten())
precision,recall,fscore = common.checkAccuracy(clf.predict(X_DynamicTest),Y_DynamicTest,[4,5,6])
print common.createConfusionMatrix(clf.predict(X_DynamicTest).flatten(),Y_DynamicTest.flatten(),[4,5,6])
print fscore
#################################################################################################################################
#Getting the dataset associated with Dynamic Activities on training
X_Dynamic,Y_Dynamic = common.getDataSubset(XFull,YFull.flatten(),[4,5,6])
#Getting the dataset associated with Dynamic Activities on testing
X_DynamicTest,Y_DynamicTest = common.getDataSubset(XFullTest,YFullTest.flatten(),[4,5,6])
X_Dynamic = common.getPowerK(X_Dynamic,[1,2,3])
X_DynamicTest = common.getPowerK(X_DynamicTest,[1,2,3])
#Fitting data using LinearSVC classifier
clf = SVC(kernel = "linear")
clf.fit(X_Dynamic, Y_Dynamic.flatten())
precision,recall,fscore = common.checkAccuracy(clf.predict(X_DynamicTest),Y_DynamicTest,[4,5,6])
print common.createConfusionMatrix(clf.predict(X_DynamicTest).flatten(),Y_DynamicTest.flatten(),[4,5,6])
print fscore
#################################################################################################################################
def LinearSVC_onGyroData():
XFull = common.parseFile('../UCI HAR Dataset/train/X_train.txt')
YFull = common.parseFile('../UCI HAR Dataset/train/y_train.txt')
XFull = common.getGyroFeatures(XFull)
#Parsing Full testing dataset
XFullTest = common.parseFile('../UCI HAR Dataset/test/X_test.txt')
YFullTest = common.parseFile('../UCI HAR Dataset/test/y_test.txt')
XFullTest = common.getGyroFeatures(XFullTest)
#################################################################################################################################
#Getting the dataset associated with Dynamic Activities on training
X_Dynamic,Y_Dynamic = common.getDataSubset(XFull,YFull.flatten(),[1,2,3,4,5,6])
#Getting the dataset associated with Dynamic Activities on testing
X_DynamicTest,Y_DynamicTest = common.getDataSubset(XFullTest,YFullTest.flatten(),[1,2,3,4,5,6])
#Fitting data using LinearSVC classifier
clf = SVC(kernel = "linear")
clf.fit(X_Dynamic, Y_Dynamic.flatten())
precision,recall,fscore = common.checkAccuracy(clf.predict(X_DynamicTest),Y_DynamicTest,[1,2,3,4,5,6])
print common.createConfusionMatrix(clf.predict(X_DynamicTest).flatten(),Y_DynamicTest.flatten(),[1,2,3,4,5,6])
print fscore
#################################################################################################################################
#Getting the dataset associated with Dynamic Activities on training
X_Dynamic,Y_Dynamic = common.getDataSubset(XFull,YFull.flatten(),[1,2,3,4,5,6])
#Getting the dataset associated with Dynamic Activities on testing
X_DynamicTest,Y_DynamicTest = common.getDataSubset(XFullTest,YFullTest.flatten(),[1,2,3,4,5,6])
X_Dynamic = common.getPowerK(X_Dynamic,[1,2])
X_DynamicTest = common.getPowerK(X_DynamicTest,[1,2])
#Fitting data using LinearSVC classifier
clf = SVC(kernel = "linear")
clf.fit(X_Dynamic, Y_Dynamic.flatten())
precision,recall,fscore = common.checkAccuracy(clf.predict(X_DynamicTest),Y_DynamicTest,[1,2,3,4,5,6])
print common.createConfusionMatrix(clf.predict(X_DynamicTest).flatten(),Y_DynamicTest.flatten(),[1,2,3,4,5,6])
print fscore
#################################################################################################################################
#Getting the dataset associated with Dynamic Activities on training
X_Dynamic,Y_Dynamic = common.getDataSubset(XFull,YFull.flatten(),[1,2,3,4,5,6])
#Getting the dataset associated with Dynamic Activities on testing
X_DynamicTest,Y_DynamicTest = common.getDataSubset(XFullTest,YFullTest.flatten(),[1,2,3,4,5,6])
X_Dynamic = common.getPowerK(X_Dynamic,[1,2,3])
X_DynamicTest = common.getPowerK(X_DynamicTest,[1,2,3])
#Fitting data using LinearSVC classifier
clf = SVC(kernel = "linear")
clf.fit(X_Dynamic, Y_Dynamic.flatten())
precision,recall,fscore = common.checkAccuracy(clf.predict(X_DynamicTest),Y_DynamicTest,[1,2,3,4,5,6])
print common.createConfusionMatrix(clf.predict(X_DynamicTest).flatten(),Y_DynamicTest.flatten(),[1,2,3,4,5,6])
print fscore
#################################################################################################################################
#Getting the dataset associated with Dynamic Activities on training
X_Dynamic,Y_Dynamic = common.getDataSubset(XFull,YFull.flatten(),[1,2,3])
#Getting the dataset associated with Dynamic Activities on testing
X_DynamicTest,Y_DynamicTest = common.getDataSubset(XFullTest,YFullTest.flatten(),[1,2,3])
print len(X_DynamicTest),len(Y_DynamicTest)
#Fitting data using LinearSVC classifier
clf = SVC(kernel = "linear")
clf.fit(X_Dynamic, Y_Dynamic.flatten())
precision,recall,fscore = common.checkAccuracy(clf.predict(X_DynamicTest),Y_DynamicTest,[1,2,3])
print common.createConfusionMatrix(clf.predict(X_DynamicTest).flatten(),Y_DynamicTest.flatten(),[1,2,3])
print fscore
#################################################################################################################################
#Getting the dataset associated with Dynamic Activities on training
X_Dynamic,Y_Dynamic = common.getDataSubset(XFull,YFull.flatten(),[1,2,3])
#Getting the dataset associated with Dynamic Activities on testing
X_DynamicTest,Y_DynamicTest = common.getDataSubset(XFullTest,YFullTest.flatten(),[1,2,3])
X_Dynamic = common.getPowerK(X_Dynamic,[1,2])
X_DynamicTest = common.getPowerK(X_DynamicTest,[1,2])
#Fitting data using LinearSVC classifier
clf = SVC(kernel = "linear")
clf.fit(X_Dynamic, Y_Dynamic.flatten())
precision,recall,fscore = common.checkAccuracy(clf.predict(X_DynamicTest),Y_DynamicTest,[1,2,3])
print common.createConfusionMatrix(clf.predict(X_DynamicTest).flatten(),Y_DynamicTest.flatten(),[1,2,3])
print fscore
#################################################################################################################################
#Getting the dataset associated with Dynamic Activities on training
X_Dynamic,Y_Dynamic = common.getDataSubset(XFull,YFull.flatten(),[1,2,3])
#Getting the dataset associated with Dynamic Activities on testing
X_DynamicTest,Y_DynamicTest = common.getDataSubset(XFullTest,YFullTest.flatten(),[1,2,3])
X_Dynamic = common.getPowerK(X_Dynamic,[1,2,3])
X_DynamicTest = common.getPowerK(X_DynamicTest,[1,2,3])
#Fitting data using LinearSVC classifier
clf = SVC(kernel = "linear")
clf.fit(X_Dynamic, Y_Dynamic.flatten())
precision,recall,fscore = common.checkAccuracy(clf.predict(X_DynamicTest),Y_DynamicTest,[1,2,3])
print common.createConfusionMatrix(clf.predict(X_DynamicTest).flatten(),Y_DynamicTest.flatten(),[1,2,3])
print fscore
#################################################################################################################################
#Getting the dataset associated with Non-Dynamic Activities on training
X_NonDynamic,Y_NonDynamic = common.getDataSubset(XFull,YFull.flatten(),[4,5,6])
#Getting the dataset associated with Non-Dynamic Activities on testing
X_NonDynamicTest,Y_NonDynamicTest = common.getDataSubset(XFullTest,YFullTest.flatten(),[4,5,6])
#Fitting data using LinearSVC classifier
clf = SVC(kernel = "linear")
clf.fit(X_NonDynamic, Y_NonDynamic.flatten())
precision,recall,fscore = common.checkAccuracy(clf.predict(X_NonDynamicTest),Y_NonDynamicTest,[4,5,6])
print common.createConfusionMatrix(clf.predict(X_NonDynamicTest).flatten(),Y_NonDynamicTest.flatten(),[4,5,6])
print fscore
#################################################################################################################################
#Getting the dataset associated with Dynamic Activities on training
X_Dynamic,Y_Dynamic = common.getDataSubset(XFull,YFull.flatten(),[4,5,6])
#Getting the dataset associated with Dynamic Activities on testing
X_DynamicTest,Y_DynamicTest = common.getDataSubset(XFullTest,YFullTest.flatten(),[4,5,6])
X_Dynamic = common.getPowerK(X_Dynamic,[1,2])
X_DynamicTest = common.getPowerK(X_DynamicTest,[1,2])
#Fitting data using LinearSVC classifier
clf = SVC(kernel = "linear")
clf.fit(X_Dynamic, Y_Dynamic.flatten())
precision,recall,fscore = common.checkAccuracy(clf.predict(X_DynamicTest),Y_DynamicTest,[4,5,6])
print common.createConfusionMatrix(clf.predict(X_DynamicTest).flatten(),Y_DynamicTest.flatten(),[4,5,6])
print fscore
#################################################################################################################################
#Getting the dataset associated with Dynamic Activities on training
X_Dynamic,Y_Dynamic = common.getDataSubset(XFull,YFull.flatten(),[4,5,6])
#Getting the dataset associated with Dynamic Activities on testing
X_DynamicTest,Y_DynamicTest = common.getDataSubset(XFullTest,YFullTest.flatten(),[4,5,6])
X_Dynamic = common.getPowerK(X_Dynamic,[1,2,3])
X_DynamicTest = common.getPowerK(X_DynamicTest,[1,2,3])
#Fitting data using LinearSVC classifier
clf = SVC(kernel = "linear")
clf.fit(X_Dynamic, Y_Dynamic.flatten())
precision,recall,fscore = common.checkAccuracy(clf.predict(X_DynamicTest),Y_DynamicTest,[4,5,6])
print common.createConfusionMatrix(clf.predict(X_DynamicTest).flatten(),Y_DynamicTest.flatten(),[4,5,6])
print fscore
#################################################################################################################################
if __name__=='__main__':
LinearSVC_onData()
#LinearSVC_onAccData()
#LinearSVC_onGyroData()