import numpy as np
import scipy as sp
import matplotlib as plt
import os as os
import sys
import glob
from sklearn import svm
from random import randint
from sklearn import multiclass


class MouseMovement:
    # Class for mouse movement data
    def __init__(self, startPixel, endPixel, listPixel=[], cl = None):
        # startPixel = [x_start, y_start, t_start]
        # endPixel = [x_end, y_end, t_end]
        # moveList = [x1, y1, t1;
        #             x2, y2, t2;
        #             x3, y3, t3;
        #               ...
        #             xn, yn, tn]
        self.start = startPixel
        self.end = endPixel
        self.moveList = listPixel
        self.cl = cl
        self.length = self.getMoveLength()
        self.moveTime = self.getMoveTime()
        # self.v = self.getSpeedVector()

    def MMPrint(self):
        print (self.start)
        print (self.moveList)
        print (self.end)

    def getMoveLength(self):
        tmp1 = self.end[0]-self.start[0]
        tmp2 = self.end[1]-self.start[1]
        length = np.sqrt(tmp1*tmp1+tmp2*tmp2)

        return length


    def getMoveTime(self):
        tmpTime=self.end[2]
        for x in self.moveList:
            tmpTime += x[2]

        return tmpTime

    def getLongPause(self):
        list = []
        list.append(self.start)
        for i in range(0, len(self.moveList)):
            list.append(self.moveList[i])
        list.append(self.end)
        tmpTime = 0
        for i in range(1, len(list)):
            if np.abs(list[i][2] - list[i - 1][2]) > 1000:
                tmpTime += list[i][2]
        return tmpTime
    def getSpeedVector(self):
        l1 = np.append(np.transpose(self.start[0:2]), self.moveList[:,0:2])
        l2 = np.append(self.moveList[:,0:2], np.transpose(self.end[0:2]))
        # print l1
        l1=l1.reshape(len(l1)/2,2)
        l2=l2.reshape(len(l2)/2,2)
        t = np.maximum(np.append(np.transpose(self.start[2]), self.moveList[:,2]),np.ones(len(self.moveList[:,2])+1))
        diff = l2 - l1
        # print diff
        diff_sq = np.multiply(diff, diff)
        # print diff_sq
        dist = np.sqrt(diff_sq[:,0]+diff_sq[:,1])
        # print dist
        v = np.divide(dist,t)
        return v

    def getAverageSpeed(self):
        n = len(self.moveList)
        avg = 0
        br = 0
        for i in range(1, n):
            tmp1 = self.moveList[i][0] - self.moveList[i - 1][0]
            tmp2 = self.moveList[i][1] - self.moveList[i - 1][1]
            tmp3 = self.moveList[i][2]
            br += np.sqrt((tmp1 * tmp1) + (tmp2 * tmp2)) / tmp3
            br += np.sqrt((self.moveList[n - 1][0] - self.end[0]) ** 2 + (self.moveList[n - 1][1] - self.end[1]) ** 2) / \
                  self.end[2]
        avg = br / (n + 1)
        return avg
    def getElement(self, id, verify):
        strOut = '-'+str(id)+'-\n'
        if verify == 1:
            strOut += '-'+str(self.cl)+'-\n'
        else:
            strOut += '---\n'
        strOut += str(self.start) + '\n'
        for data in self.moveList:
            strOut += str(data) + '\n'
        strOut += str(self.end) + '\n'

        return strOut


moveClassList = []

# Path to data file
data = str('hw2_mouse_data.txt')
f = open(data,'r')
cnt = 0
tmp = '0'
move_list = []
y=[]
# def feature_normalize(x):
#     x_norm=x
#     num_col=x.shape[1]
#     for i in range (1,num_col):
#         x_norm[:,i]=(x_norm[:,i]-np.mean(x[:,i]))/np.std(x[:,i])
#     return x_norm
while tmp != '':
    # read new line
    tmp = f.readline()

    if not tmp:
     #   moveClassList.append(MouseMovement(move_list[0], move_list[-1], move_list[1:-1], cl))
        break
    if tmp[0] == '-':
        # save the data
        if cnt >0:
            moveClassList.append(MouseMovement(move_list[0], move_list[-1], move_list[1:-1], cl))
        cnt += 1

        tmp = f.readline()

        if tmp.find('Goran', 0, len(tmp)) >=0:
            cl = 0  #'Goran'
            y.append(cl)

        if tmp.find('Gorjan', 0, len(tmp))>=0:
            cl = 1  #'Gorjan'
            y.append(cl)

        if tmp.find('Ivan', 0, len(tmp))>=0:
            cl = 2  #'Ivan'
            y.append(cl)

        if tmp.find('Georgi', 0, len(tmp))>=0:
            cl = 3  #'Georgi'
            y.append(cl)


        move_list = []

    else:
        if tmp[0] == '[':
            data = tmp.replace('[','')
            data = data.replace(']','')
            data = data.replace('\n','')

            value = [int(no) for no in data.split(",")]
            move_list.append(value)


# print test data
#print (moveClassList[3].getElement(4,0))

#print (moveClassList[3].getAverageSpeed())



# model = svm.SVC(C=10.0, kernel='rbf', degree=3, gamma=1, coef0=0.0, shrinking=True, probability=False,tol=0.001, cache_size=200, class_weight=None, verbose=False, max_iter=-1, random_state=5)
# model.fit(x_train, y_train)
# model.score(x_train, y_train)
# predicted = model.predict(x_predict)


# t1_str = str('./data_ready/hw2_mouse_prediction_fin.txt')
# f1 = open(t1_str,'w')
# t2_str = str('./data_ready/hw2_mouse_prediction-verify_fin.txt')
# f2 = open(t2_str,'w')
# cnt = 1
#
# while not (not moveClassList):
#     el = np.random.randint(0,len(moveClassList),1)
#     print el
#     f1.write(moveClassList[el].getElement(cnt, 0)) # test
#     f2.write(moveClassList[el].getElement(cnt, 1)) # verify
#     del moveClassList[el]
#     cnt += 1
#     print cnt
#
#
moveClassList2 = []
data_predict = str('hw2_mouse_prediction.txt')
f2 = open(data_predict,'r')
cnt2 = 0
tmp2 = '0'
move_list2 = []

while tmp2 != '':
    # read new line
    tmp2 = f2.readline()
    if not tmp2:
        moveClassList2.append(MouseMovement(move_list2[0], move_list2[-1], move_list2[1:-1]))
        break
    if tmp2[0] == '-':
        # save the data
        if cnt2 >0:
            moveClassList2.append(MouseMovement(move_list2[0], move_list2[-1], move_list2[1:-1]))
        cnt2 += 1
        tmp2 = f2.readline()
        move_list2 = []
    else:
        if tmp2[0] == '[':
            data_predict = tmp2.replace('[','')
            data_predict = data_predict.replace(']','')
            data_predict = data_predict.replace('\n','')
            value = [int(no) for no in data_predict.split(",")]
            move_list2.append(value)
speeds = []
moveLengths = []
moveTimes = []
print(len(moveClassList))
for i in range(0, len(moveClassList)):
    speeds.append(moveClassList[i].getAverageSpeed())
    moveLengths.append((moveClassList[i].getMoveLength()))
    moveTimes.append((moveClassList[i].getMoveTime()))
data_train = np.zeros((1151, 4))
data_train[:, 0] = speeds
data_train[:, 1] = moveLengths
data_train[:, 2] = moveTimes
data_train[:, 3]=y

for i in range(0,1151):
    rand = randint(0,1150)
    pom = data_train[rand,:]
    data_train[rand,:] = data_train[i,:]
    data_train[i,:] = pom

x_train = np.zeros((1151, 4))
y_train = []
x_train = data_train[:, :3]
y_train = data_train[:, 3]

#x_test = np.zeros((231, 2))
#y_test = []
#x_test = data_train[921:, :2]
#y_test = data_train[921:, 2]
speedsP = []
moveLengthsP = []
moveTimesP = []
#print(len(moveClassList2))
for c in range(0, len(moveClassList2)):
    speedsP.append(moveClassList2[c].getAverageSpeed())
    moveLengthsP.append((moveClassList2[c].getMoveLength()))
    moveTimesP.append((moveClassList2[c].getMoveTime()))
data_p = np.zeros((133, 3))
data_p[:, 0] = speedsP
data_p[:, 1] = moveLengthsP
data_p[:, 2]=moveTimesP


#data_train[:, 2] = y


x_p = np.zeros((133, 3))
x_p = data_p[:, :]


classif = multiclass.OneVsRestClassifier(svm.SVC(C=1.0, kernel='rbf', degree=3, gamma=0.00005, coef0=0.0, shrinking=True, probability=False,tol=0.001, cache_size=200, class_weight=None, verbose=False, max_iter=-1, random_state=1))
classif.fit(x_train, y_train)
#score = classif.score(x_test, y_test)
#predicted = classif.predict(x_test)
#predict= classif.predict(x_p)
#for i in range(0, len(predicted)):
 #   print(predicted[i])
#print("Procent pogodeni: ", score*100)

predict1 = classif.predict(x_p)
eden=0
dva=0
tri=0
nula=0
for i in range(0, len(predict1)):
    if (predict1[i] == 0):
        nula += 1
    if (predict1[i] == 1):
        eden += 1
    if (predict1[i] == 2):
        dva += 1
    if (predict1[i] == 3):
        tri += 1
    #print(predict1[i])

for i in range(0,133):
    if predict1[i] == 0:
        moveClassList2[i].cl = 'Goran'
    if predict1[i] == 1:
        moveClassList2[i].cl = 'Gorjan'
    if predict1[i] == 2:
        moveClassList2[i].cl = 'Ivan'
    if predict1[i] == 3:
        moveClassList2[i].cl = 'Georgi'
for i in range(0,133):
    with open("hw_mouse_prediction_006_2013.txt", "a") as myfile:
        myfile.write(moveClassList2[i].getElement(i+1,1))
print(nula)
print(eden)
print(dva)
print(tri)