import random
from numpy import *
from Tkinter import *
from helperFunctions import *
import ball_simulation
import filter
import time

X = filter.data
Y = filter.target
X_len = len(X)
print 'lenX',len(X),'lenY',len(Y)


from sklearn.preprocessing import scale
X = scale(X)
# from sklearn.preprocessing import normalize
# X = normalize(X)
#print '*******************************************************',X[0],'****************************************************'
from sklearn.decomposition import PCA
pca = PCA(n_components=4)

X = pca.fit_transform(X)
# print '///////////////////////',X[0],'//////////////////////'
# print '/////',len(X)
# print '*****',len(X[0])

from sklearn.cross_validation import train_test_split

X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = .2)
len_Y_train = len(Y_train)

# from sklearn.neural_network import MLPClassifier
# my_classifier = MLPClassifier(hidden_layer_sizes=(20), activation='logistic', alpha=0.0001, learning_rate='invscaling',
# 					max_iter=20, tol=0.00000001, verbose=True, warm_start=True)

# from sklearn.cluster import KMeans
# my_classifier = KMeans(n_clusters=2, n_init=10, n_jobs=-1)

from sklearn.svm import SVC
my_classifier = SVC()

# from sklearn import tree
# my_classifier = tree.DecisionTreeClassifier()

# for i in range(200):
# 	print i
my_classifier.fit(X_train, Y_train)

predictions = my_classifier.predict(X_test)

##### Print prediction and move the ball in the simulation

# initialize root Window and canvas
# root = Tk()
# root.title("Balls")
# root.resizable(False,False)
# canvas = Canvas(root, width = 700, height = 700)
# canvas.pack()

# # create two ball objects and animate them
# ball1 = ball_simulation.Ball(canvas, 300, 300, 350, 350)

for i in range(len(Y_test)):
	# root.update()
	# if predictions[i] == Y_test[i]:
	print predictions[i], Y_test[i]#, (predictions[i]-0.5)*10
	# else:
	# 	print 'no'
	# # ball1.move_ball((predictions[i]-0.5)*50)
	# time.sleep(.200)

	#root.mainloop()

from sklearn.metrics import accuracy_score
accuracy = accuracy_score(Y_test, predictions)
print accuracy

##########################	
import random
from numpy import *
from Tkinter import *
from helperFunctions import *
import ball_simulation
import filter
import time

X = filter.data
Y = filter.target
X_len = len(X)
print 'lenX',len(X),'lenY',len(Y)


from sklearn.preprocessing import scale
X = scale(X)
# from sklearn.preprocessing import normalize
# X = normalize(X)
#print '*******************************************************',X[0],'****************************************************'
from sklearn.decomposition import PCA
pca = PCA(n_components=4)

X = pca.fit_transform(X)
# print '///////////////////////',X[0],'//////////////////////'
# print '/////',len(X)
# print '*****',len(X[0])

from sklearn.cross_validation import train_test_split

X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = .2)
len_Y_train = len(Y_train)

# from sklearn.neural_network import MLPClassifier
# my_classifier = MLPClassifier(hidden_layer_sizes=(20), activation='logistic', alpha=0.0001, learning_rate='invscaling',
# 					max_iter=20, tol=0.00000001, verbose=True, warm_start=True)

# from sklearn.cluster import KMeans
# my_classifier = KMeans(n_clusters=2, n_init=10, n_jobs=-1)

from sklearn.svm import SVC
my_classifier = SVC()

# from sklearn import tree
# my_classifier = tree.DecisionTreeClassifier()

# for i in range(200):
# 	print i
my_classifier.fit(X_train, Y_train)

predictions = my_classifier.predict(X_test)

##### Print prediction and move the ball in the simulation

# initialize root Window and canvas
# root = Tk()
# root.title("Balls")
# root.resizable(False,False)
# canvas = Canvas(root, width = 700, height = 700)
# canvas.pack()

# # create two ball objects and animate them
# ball1 = ball_simulation.Ball(canvas, 300, 300, 350, 350)

for i in range(len(Y_test)):
	# root.update()
	# if predictions[i] == Y_test[i]:
	print predictions[i], Y_test[i]#, (predictions[i]-0.5)*10
	# else:
	# 	print 'no'
	# # ball1.move_ball((predictions[i]-0.5)*50)
	# time.sleep(.200)

	#root.mainloop()

from sklearn.metrics import accuracy_score
accuracy = accuracy_score(Y_test, predictions)
print accuracy

##########################	
# from sklearn.preprocessing import normalize # X = normalize(X) #print '*******************************************************',X[0],'****************************************************' # print '///////////////////////',X[0],'//////////////////////' # print '/////',len(X) # print '*****',len(X[0]) # from sklearn.neural_network import MLPClassifier # my_classifier = MLPClassifier(hidden_layer_sizes=(20), activation='logistic', alpha=0.0001, learning_rate='invscaling', # 					max_iter=20, tol=0.00000001, verbose=True, warm_start=True) # from sklearn.cluster import KMeans # my_classifier = KMeans(n_clusters=2, n_init=10, n_jobs=-1) # from sklearn import tree # my_classifier = tree.DecisionTreeClassifier() # for i in range(200): # 	print i ##### Print prediction and move the ball in the simulation # initialize root Window and canvas # root = Tk() # root.title("Balls") # root.resizable(False,False) # canvas = Canvas(root, width = 700, height = 700) # canvas.pack() # # create two ball objects and animate them # ball1 = ball_simulation.Ball(canvas, 300, 300, 350, 350) # root.update() # if predictions[i] == Y_test[i]: #, (predictions[i]-0.5)*10 # else: # 	print 'no' # # ball1.move_ball((predictions[i]-0.5)*50) # time.sleep(.200) #root.mainloop() ##########################