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keras_model.py
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keras_model.py
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# Copyright 2017 Dhvani Patel
from keras.models import Sequential
from keras.layers import Dense, Dropout, Input, Embedding, LSTM, Reshape, Flatten, Activation
from keras.models import Model
from keras import optimizers
from keras.callbacks import ModelCheckpoint, CSVLogger, EarlyStopping
from keras import regularizers
import numpy
from Token import Token
from py_mutations_hub import perform
from skimage.io import imread
from skimage.transform import resize
import numpy as np
from itertools import izip_longest
from itertools import izip
import os
import cPickle
import matplotlib.pyplot as plt
import sys
# BATCH = 60
# So 15 of 4 of one window
# One 4:
# [Good, Insert, Delete, Sub]
WINDOW_SIZE = 10
# DEFINED:
# FIXED INPUT BITS = 87 (ONE TOKEN)
# FIXED OUTPUT BITS = 102 (2+2+3+10+85)
# WINDOW = 10, SO BATCH = 40 INPUT, 40 OUTPUT
BATCH_SIZE = 66
def one_hot(indexed_tokens):
one_hot = []
nb_classes = 88
one_hot_targets = np.eye(nb_classes)[indexed_tokens]
one_hot = one_hot_targets.tolist()
#print "fort"
#bruhTemp = one_hot[:]
for x in range(len(one_hot)):
#one_hot[x].astype(int)
[int(i) for i in one_hot[x]]
#one_hot.astype(int)
#print type(one_hot[0][0])
return one_hot
def getInputTen(allTrainData):
#one_hot_good, one_hot_bad_ins, one_hot_bad_del, one_hot_bad_sub, _, _, _, _, _ = perform(0)
one_hot_good, one_hot_bad_ins, one_hot_bad_del, one_hot_bad_sub = one_hot(allTrainData[0][0]),one_hot(allTrainData[0][1]), one_hot(allTrainData[0][2]), one_hot(allTrainData[0][3])
while(one_hot_good == 1):
one_hot_good, one_hot_bad_ins, one_hot_bad_del, one_hot_bad_sub = one_hot(allTrainData[0][0]),one_hot(allTrainData[0][1]), one_hot(allTrainData[0][2]), one_hot(allTrainData[0][3])
#print type(one_hot_good)
#print one_hot_good
windowInd = 0
fileInd = 0
batchInd = 1
#count = 0
while True: # 462540
#while windowInd < int(len(insArr)/10):
if fileInd >= 1000:
fileInd = 0
#print "file"
#print fileInd
#print minSize
#print windowInd
#print int((int(minSize) / 10))
loopInd = 0
batchArr = []
while loopInd < (BATCH_SIZE / 3):
sizes = [len(one_hot_good), len(one_hot_bad_ins),len(one_hot_bad_del),len(one_hot_bad_sub)]
minSize = min(float(siz) for siz in sizes) # min of a generator
if windowInd < int((int(minSize) - 10)):
#print windowInd
#print "WINDOW"
batchInd = 1
#print len(one_hot_good)
#print len(one_hot_bad_ins)
#print len(one_hot_bad_del)
#print len(one_hot_bad_sub)
toPassOne = []
for x in range(10):
y = x + windowInd
if y < len(one_hot_good):
toPassOne.append(one_hot_good[y])
toPassTwo = []
for x in range(10):
y = x + windowInd
if y < len(one_hot_bad_ins):
toPassTwo.append(one_hot_bad_ins[y])
toPassThree = []
for x in range(10):
y = x + windowInd
if y < len(one_hot_bad_del):
toPassThree.append(one_hot_bad_del[y])
toPassFour = []
for x in range(10):
y = x + windowInd
if y < len(one_hot_bad_sub):
toPassFour.append(one_hot_bad_sub[y])
#print "TEST"
#print numpy.array(toPassOne).astype(int)[0]
#print len(toPass)
#print len(toPassOne)
#print len(toPassTwo)
#print len(toPassThree)
#print len(toPassFour)
#toPass = np.array((,, ))
#toPass = np.concatenate((numpy.array(toPassOne).astype(int), numpy.array(toPassTwo).astype(int), numpy.array(toPassThree).astype(int)), axis=0)
#a = numpy.array(toPass).astype(int)
#print toPass.shape
#toPass = []
while(batchInd % 4 != 0):
toPass = []
#print "BATCH IND"
#print batchInd
if(batchInd == 1):
toPass = toPassOne[:]
elif(batchInd == 2):
toPass = toPassTwo[:]
elif(batchInd == 3):
toPass = toPassThree[:]
elif(batchInd == 4):
print "here"
toPass = toPassTwo[:]
#print toPass
#print [toPass]
a = numpy.array(toPass).astype(int)
#print a.shape
#count+=1
#print "COUNT"
#print count
#print b.shape
#print a
batchArr.append(a)
batchInd += 1
#print numpy.array(toPass).shape
#print "mine too"
#a = numpy.array(toPass)
#print a.shape
#yield a
windowInd += 1
#print loopInd
loopInd += 1
else:
old_one_hot_good = one_hot_good[:]
old_one_hot_bad_ins = one_hot_bad_ins[:]
old_one_hot_bad_del = one_hot_bad_del[:]
old_one_hot_bad_sub = one_hot_bad_sub[:]
numGoodLeft = len(one_hot_good) % 10
numBadInsLeft = len(one_hot_bad_ins) % 10
numBadDelLeft = len(one_hot_bad_del) % 10
numBadSubLeft = len(one_hot_bad_sub) % 10
fileInd += 1
if fileInd >= 1000:
fileInd = 0
#print "FILE IND"
#print fileInd
windowInd = 0
#one_hot_good, one_hot_bad_ins, one_hot_bad_del, one_hot_bad_sub, _, _, _, _, _ = perform(fileInd)
one_hot_good = allTrainData[fileInd]
while(one_hot_good == -1):
fileInd+=1
if fileInd >= 1000:
fileInd = 0
one_hot_good, one_hot_bad_ins, one_hot_bad_del, one_hot_bad_sub = one_hot(allTrainData[fileInd][0]),one_hot(allTrainData[fileInd][1]), one_hot(allTrainData[fileInd][2]), one_hot(allTrainData[fileInd][3])
if one_hot_good != -1:
one_hot_good, one_hot_bad_ins, one_hot_bad_del, one_hot_bad_sub = one_hot(allTrainData[fileInd][0]),one_hot(allTrainData[fileInd][1]), one_hot(allTrainData[fileInd][2]), one_hot(allTrainData[fileInd][3])
for p in range(numGoodLeft):
one_hot_good.insert(p, old_one_hot_good[len(old_one_hot_good)-numGoodLeft+p])
for p in range(numBadInsLeft):
one_hot_bad_ins.insert(p, old_one_hot_bad_ins[len(old_one_hot_bad_ins)-numBadInsLeft+p])
for p in range(numBadDelLeft):
one_hot_bad_del.insert(p, old_one_hot_bad_del[len(old_one_hot_bad_del)-numBadDelLeft+p])
for p in range(numBadSubLeft):
one_hot_bad_sub.insert(p, old_one_hot_bad_sub[len(old_one_hot_bad_sub)-numBadSubLeft+p])
#print numpy.array(batchArr).shape
#print len(batchArr)
#print "dhvani"
b = numpy.array(batchArr)
#print b.shape
yield b
#else:
#print "NEXT FILE"
#print "DONE BRO"
'''
old_one_hot_good = one_hot_good[:]
old_one_hot_bad_ins = one_hot_bad_ins[:]
old_one_hot_bad_del = one_hot_bad_del[:]
old_one_hot_bad_sub = one_hot_bad_sub[:]
numGoodLeft = len(one_hot_good) % 10
numBadInsLeft = len(one_hot_bad_ins) % 10
numBadDelLeft = len(one_hot_bad_del) % 10
numBadSubLeft = len(one_hot_bad_sub) % 10
fileInd += 1
print "FILE IND"
print fileInd
windowInd = 0
one_hot_good, one_hot_bad_ins, one_hot_bad_del, one_hot_bad_sub, _, _, _, _, _ = perform(fileInd)
while(one_hot_good == 1):
fileInd+=1
one_hot_good, one_hot_bad_ins, one_hot_bad_del, one_hot_bad_sub, _, _, _, _, _ = perform(fileInd)
for p in range(numGoodLeft):
one_hot_good.insert(p, old_one_hot_good[len(old_one_hot_good)-numGoodLeft+p])
for p in range(numBadInsLeft):
one_hot_bad_ins.insert(p, old_one_hot_bad_ins[len(old_one_hot_bad_ins)-numBadInsLeft+p])
for p in range(numBadDelLeft):
one_hot_bad_del.insert(p, old_one_hot_bad_del[len(old_one_hot_bad_del)-numBadDelLeft+p])
for p in range(numBadSubLeft):
one_hot_bad_sub.insert(p, old_one_hot_bad_sub[len(old_one_hot_bad_sub)-numBadSubLeft+p])
'''
def getOutputTen(allTrainData):
#_, _, _, _, one_hot_good_out, one_hot_bad_ins_out, one_hot_bad_del_out, one_hot_bad_sub_out, _ = perform(0)
one_hot_good_out, one_hot_bad_ins_out, one_hot_bad_del_out, one_hot_bad_sub_out = one_hot(allTrainData[0][0]),one_hot(allTrainData[0][1]), one_hot(allTrainData[0][2]), one_hot(allTrainData[0][3])
while(one_hot_good_out == 1):
#_, _, _, _, one_hot_good_out, one_hot_bad_ins_out, one_hot_bad_del_out, one_hot_bad_sub_out, _ = perform(0)
one_hot_good_out, one_hot_bad_ins_out, one_hot_bad_del_out, one_hot_bad_sub_out = one_hot(allTrainData[0][0]),one_hot(allTrainData[0][1]), one_hot(allTrainData[0][2]), one_hot(allTrainData[0][3])
#print type(one_hot_good_out)
#print one_hot_good_out
windowInd = 0
fileInd = 0
batchInd = 1
while True: # 462540
#while windowInd < int(len(insArr)/10):
if fileInd >= 1000:
fileInd = 0
loopInd = 0
batchArr = []
while loopInd < (BATCH_SIZE / 3):
sizes = [len(one_hot_good_out), len(one_hot_bad_ins_out),len(one_hot_bad_del_out),len(one_hot_bad_sub_out)]
minSize = min(float(siz) for siz in sizes) # min of a generator
if windowInd < int((int(minSize)-10)):
batchInd = 1
toPassOne = []
#for x in range(10):
y = windowInd
if y < len(one_hot_good_out):
err = one_hot_good_out[y][0]
clasF = one_hot_good_out[y][4]
clasF = one_hot_good_out[y][5]
clasF = one_hot_good_out[y][6]
#bruhOne = []
if True:
zero = 1
toPassOne.append(zero)
one = 0
toPassOne.append(one)
two = 0
toPassOne.append(two)
three = 0
toPassOne.append(three)
else:
print "not here"
#toPassOne.append(bruhOne)
#print bruhOne
#print "TO PASS"
#print toPassOne
toPassTwo = []
#for x in range(10):
y = windowInd
if y < len(one_hot_bad_ins_out):
err = one_hot_bad_ins_out[y][0]
clasF = one_hot_bad_ins_out[y][4]
clasF = one_hot_bad_ins_out[y][5]
clasF = one_hot_bad_ins_out[y][6]
#bruhTwo = []
if True:
zero = 0
toPassTwo.append(zero)
one = 0
toPassTwo.append(one)
two = 0
toPassTwo.append(two)
three = 1
toPassTwo.append(three)
else:
print "GET OUT OF HERE"
print type(radha)
#toPassTwo.append(bruhTwo)
toPassThree = []
#for x in range(10):
y = windowInd
if y < len(one_hot_bad_del_out):
#toPassThree.append(one_hot_bad_del_out[y])
err = one_hot_bad_del_out[y][0]
clasF = one_hot_bad_del_out[y][4]
clasF = one_hot_bad_del_out[y][5]
clasF = one_hot_bad_del_out[y][6]
#bruhThree = []
if True:
zero = 0
toPassThree.append(zero)
one = 0
toPassThree.append(one)
two = 1
toPassThree.append(two)
three = 0
toPassThree.append(three)
else:
print "GET OUT OF HERE"
print type(radha)
#toPassThree.append(bruhThree)
toPassFour = []
#for x in range(10):
y = windowInd
if y < len(one_hot_bad_sub_out):
#toPassFour.append(one_hot_bad_sub_out[y])
err = one_hot_bad_sub_out[y][0]
clasF = one_hot_bad_sub_out[y][4]
clasF = one_hot_bad_sub_out[y][5]
clasF = one_hot_bad_sub_out[y][6]
#bruhFour = []
if True:
zero = 0
toPassFour.append(zero)
one = 1
toPassFour.append(one)
two = 0
toPassFour.append(two)
three = 0
toPassFour.append(three)
else:
zero = 1
toPassFour.append(zero)
one = 0
toPassFour.append(one)
two = 0
toPassFour.append(two)
three = 0
toPassFour.append(three)
#toPassFour.append(bruhFour)
#print "TEST OUT"
#print toPassOne[0]
#print len(toPass)
#toPass = np.array((toPassOne, toPassTwo, toPassThree, toPassFour))
#print toPass.shape
while(batchInd % 4 != 0):
toPass = []
#print "BATCH IND"
#print batchInd
if(batchInd == 1):
toPass = toPassOne[:]
elif(batchInd == 2):
toPass = toPassTwo[:]
elif(batchInd == 3):
toPass = toPassThree[:]
elif(batchInd == 4):
print "here"
toPass = toPassTwo[:]
#print len(toPass)
#print toPassOne[1]
a = numpy.array(toPass)
#a = b[None, :]
#print a.shape
#print numpy.array(toPassOne)
#print a
#count+=1
#print "COUNT"
#print count
#print b.shape
#print a
#yield a
batchArr.append(a)
batchInd += 1
#toPass = np.concatenate((numpy.array(toPassOne).astype(int), numpy.array(toPassTwo).astype(int), numpy.array(toPassThree).astype(int)), axis=0)
#a = numpy.array(toPass)
#yield a
windowInd += 1
#print loopInd
loopInd += 1
else:
#print "NEXT FILE"
#old_one_hot_good_out = one_hot_good_out[:]
#old_one_hot_bad_ins_out = one_hot_bad_ins_out[:]
#old_one_hot_bad_del_out = one_hot_bad_del_out[:]
#old_one_hot_bad_sub_out = one_hot_bad_sub_out[:]
#numGoodOutLeft = len(one_hot_good_out) % 10
#numBadInsOutLeft = len(one_hot_bad_ins_out) % 10
#numBadDelOutLeft = len(one_hot_bad_del_out) % 10
#numBadSubOutLeft = len(one_hot_bad_sub_out) % 10
fileInd += 1
if fileInd >= 1000:
fileInd = 0
windowInd = 0
#_, _, _, _, one_hot_good_out, one_hot_bad_ins_out, one_hot_bad_del_out, one_hot_bad_sub_out, _ = perform(fileInd)
print fileInd
#print allTrainData[37]
#print type(allTrainData[fileInd][1])
#print type(allTrainData[fileInd][2])
#print type(allTrainData[fileInd][3])
one_hot_good_out = allTrainData[fileInd]
while(one_hot_good_out == -1):
fileInd+=1
if fileInd >= 1000:
fileInd = 0
#_, _, _, _, one_hot_good_out, one_hot_bad_ins_out, one_hot_bad_del_out, one_hot_bad_sub_out, _ = perform(fileInd)
one_hot_good_out, one_hot_bad_ins_out, one_hot_bad_del_out, one_hot_bad_sub_out = one_hot(allTrainData[fileInd][0]),one_hot(allTrainData[fileInd][1]), one_hot(allTrainData[fileInd][2]), one_hot(allTrainData[fileInd][3])
if one_hot_good_out != -1:
one_hot_good_out, one_hot_bad_ins_out, one_hot_bad_del_out, one_hot_bad_sub_out = one_hot(allTrainData[fileInd][0]),one_hot(allTrainData[fileInd][1]), one_hot(allTrainData[fileInd][2]), one_hot(allTrainData[fileInd][3])
#for p in range(numGoodOutLeft):
# one_hot_good_out.insert(p, old_one_hot_good_out[len(old_one_hot_good_out)-numGoodOutLeft+p])
#for p in range(numBadInsOutLeft):
# one_hot_bad_ins_out.insert(p, old_one_hot_bad_ins_out[len(old_one_hot_bad_ins_out)-numBadInsOutLeft+p])
#for p in range(numBadDelOutLeft):
# one_hot_bad_del_out.insert(p, old_one_hot_bad_del_out[len(old_one_hot_bad_del_out)-numBadDelOutLeft+p])
#for p in range(numBadSubOutLeft):
# one_hot_bad_sub_out.insert(p, old_one_hot_bad_sub_out[len(old_one_hot_bad_sub_out)-numBadSubOutLeft+p])
#print loopInd
#loopInd += 1
#print numpy.array(batchArr).shape
#print len(batchArr)
#print "dhvani"
b = numpy.array(batchArr)
#print b.shape
yield b
def getInputValTen(allValData):
#one_hot_good, one_hot_bad_ins, one_hot_bad_del, one_hot_bad_sub, _, _, _, _, _ = perform(0)
one_hot_good, one_hot_bad_ins, one_hot_bad_del, one_hot_bad_sub = one_hot(allValData[0][0]),one_hot(allValData[0][1]), one_hot(allValData[0][2]), one_hot(allValData[0][3])
while(one_hot_good == 1):
one_hot_good, one_hot_bad_ins, one_hot_bad_del, one_hot_bad_sub = one_hot(allValData[0][0]),one_hot(allValData[0][1]), one_hot(allValData[0][2]), one_hot(allValData[0][3])
#print type(one_hot_good)
#print one_hot_good
windowInd = 0
fileInd = 0
batchInd = 1
#count = 0
while True: # 462540
#while windowInd < int(len(insArr)/10):
if fileInd >= 1000:
fileInd = 0
#print "file"
#print fileInd
#print minSize
#print windowInd
#print int((int(minSize) / 10))
loopInd = 0
batchArr = []
while loopInd < (BATCH_SIZE / 3):
sizes = [len(one_hot_good), len(one_hot_bad_ins),len(one_hot_bad_del),len(one_hot_bad_sub)]
minSize = min(float(siz) for siz in sizes) # min of a generator
if windowInd < int((int(minSize) - 10)):
#print windowInd
#print "WINDOW"
batchInd = 1
#print len(one_hot_good)
#print len(one_hot_bad_ins)
#print len(one_hot_bad_del)
#print len(one_hot_bad_sub)
toPassOne = []
for x in range(10):
y = x + windowInd
if y < len(one_hot_good):
toPassOne.append(one_hot_good[y])
toPassTwo = []
for x in range(10):
y = x + windowInd
if y < len(one_hot_bad_ins):
toPassTwo.append(one_hot_bad_ins[y])
toPassThree = []
for x in range(10):
y = x + windowInd
if y < len(one_hot_bad_del):
toPassThree.append(one_hot_bad_del[y])
toPassFour = []
for x in range(10):
y = x + windowInd
if y < len(one_hot_bad_sub):
toPassFour.append(one_hot_bad_sub[y])
#print "TEST"
#print numpy.array(toPassOne).astype(int)[0]
#print len(toPass)
#print len(toPassOne)
#print len(toPassTwo)
#print len(toPassThree)
#print len(toPassFour)
#toPass = np.array((,, ))
#toPass = np.concatenate((numpy.array(toPassOne).astype(int), numpy.array(toPassTwo).astype(int), numpy.array(toPassThree).astype(int)), axis=0)
#a = numpy.array(toPass).astype(int)
#print toPass.shape
#toPass = []
while(batchInd % 4 != 0):
toPass = []
#print "BATCH IND"
#print batchInd
if(batchInd == 1):
toPass = toPassOne[:]
elif(batchInd == 2):
toPass = toPassTwo[:]
elif(batchInd == 3):
toPass = toPassThree[:]
elif(batchInd == 4):
print "here"
toPass = toPassTwo[:]
#print toPass
#print [toPass]
a = numpy.array(toPass).astype(int)
#print a.shape
#count+=1
#print "COUNT"
#print count
#print b.shape
#print a
batchArr.append(a)
batchInd += 1
#print numpy.array(toPass).shape
#print "mine too"
#a = numpy.array(toPass)
#print a.shape
#yield a
windowInd += 1
#print loopInd
loopInd += 1
else:
old_one_hot_good = one_hot_good[:]
old_one_hot_bad_ins = one_hot_bad_ins[:]
old_one_hot_bad_del = one_hot_bad_del[:]
old_one_hot_bad_sub = one_hot_bad_sub[:]
numGoodLeft = len(one_hot_good) % 10
numBadInsLeft = len(one_hot_bad_ins) % 10
numBadDelLeft = len(one_hot_bad_del) % 10
numBadSubLeft = len(one_hot_bad_sub) % 10
fileInd += 1
if fileInd >= 1000:
fileInd = 0
#print "FILE IND"
#print fileInd
windowInd = 0
#one_hot_good, one_hot_bad_ins, one_hot_bad_del, one_hot_bad_sub, _, _, _, _, _ = perform(fileInd)
one_hot_good = allValData[fileInd]
while(one_hot_good == -1):
fileInd+=1
if fileInd >= 1000:
fileInd = 0
one_hot_good, one_hot_bad_ins, one_hot_bad_del, one_hot_bad_sub = one_hot(allValData[fileInd][0]),one_hot(allValData[fileInd][1]), one_hot(allValData[fileInd][2]), one_hot(allValData[fileInd][3])
if one_hot_good != -1:
one_hot_good, one_hot_bad_ins, one_hot_bad_del, one_hot_bad_sub = one_hot(allValData[fileInd][0]),one_hot(allValData[fileInd][1]), one_hot(allValData[fileInd][2]), one_hot(allValData[fileInd][3])
for p in range(numGoodLeft):
one_hot_good.insert(p, old_one_hot_good[len(old_one_hot_good)-numGoodLeft+p])
for p in range(numBadInsLeft):
one_hot_bad_ins.insert(p, old_one_hot_bad_ins[len(old_one_hot_bad_ins)-numBadInsLeft+p])
for p in range(numBadDelLeft):
one_hot_bad_del.insert(p, old_one_hot_bad_del[len(old_one_hot_bad_del)-numBadDelLeft+p])
for p in range(numBadSubLeft):
one_hot_bad_sub.insert(p, old_one_hot_bad_sub[len(old_one_hot_bad_sub)-numBadSubLeft+p])
#print numpy.array(batchArr).shape
#print len(batchArr)
#print "dhvani"
b = numpy.array(batchArr)
#print b.shape
yield b
#else:
#print "NEXT FILE"
#print "DONE BRO"
'''
old_one_hot_good = one_hot_good[:]
old_one_hot_bad_ins = one_hot_bad_ins[:]
old_one_hot_bad_del = one_hot_bad_del[:]
old_one_hot_bad_sub = one_hot_bad_sub[:]
numGoodLeft = len(one_hot_good) % 10
numBadInsLeft = len(one_hot_bad_ins) % 10
numBadDelLeft = len(one_hot_bad_del) % 10
numBadSubLeft = len(one_hot_bad_sub) % 10
fileInd += 1
print "FILE IND"
print fileInd
windowInd = 0
one_hot_good, one_hot_bad_ins, one_hot_bad_del, one_hot_bad_sub, _, _, _, _, _ = perform(fileInd)
while(one_hot_good == 1):
fileInd+=1
one_hot_good, one_hot_bad_ins, one_hot_bad_del, one_hot_bad_sub, _, _, _, _, _ = perform(fileInd)
for p in range(numGoodLeft):
one_hot_good.insert(p, old_one_hot_good[len(old_one_hot_good)-numGoodLeft+p])
for p in range(numBadInsLeft):
one_hot_bad_ins.insert(p, old_one_hot_bad_ins[len(old_one_hot_bad_ins)-numBadInsLeft+p])
for p in range(numBadDelLeft):
one_hot_bad_del.insert(p, old_one_hot_bad_del[len(old_one_hot_bad_del)-numBadDelLeft+p])
for p in range(numBadSubLeft):
one_hot_bad_sub.insert(p, old_one_hot_bad_sub[len(old_one_hot_bad_sub)-numBadSubLeft+p])
'''
def getOutputValTen(allValData):
#_, _, _, _, one_hot_good_out, one_hot_bad_ins_out, one_hot_bad_del_out, one_hot_bad_sub_out, _ = perform(0)
one_hot_good_out, one_hot_bad_ins_out, one_hot_bad_del_out, one_hot_bad_sub_out = one_hot(allValData[0][0]),one_hot(allValData[0][1]), one_hot(allValData[0][2]), one_hot(allValData[0][3])
while(one_hot_good_out == 1):
#_, _, _, _, one_hot_good_out, one_hot_bad_ins_out, one_hot_bad_del_out, one_hot_bad_sub_out, _ = perform(0)
one_hot_good_out, one_hot_bad_ins_out, one_hot_bad_del_out, one_hot_bad_sub_out = one_hot(allValData[0][0]),one_hot(allValData[0][1]), one_hot(allValData[0][2]), one_hot(allValData[0][3])
#print type(one_hot_good_out)
#print one_hot_good_out
windowInd = 0
fileInd = 0
batchInd = 1
while True: # 462540
#while windowInd < int(len(insArr)/10):
if fileInd >= 1000:
fileInd = 0
loopInd = 0
batchArr = []
while loopInd < (BATCH_SIZE / 3):
sizes = [len(one_hot_good_out), len(one_hot_bad_ins_out),len(one_hot_bad_del_out),len(one_hot_bad_sub_out)]
minSize = min(float(siz) for siz in sizes) # min of a generator
if windowInd < int((int(minSize)-10)):
batchInd = 1
toPassOne = []
#for x in range(10):
y = windowInd
if y < len(one_hot_good_out):
err = one_hot_good_out[y][0]
clasF = one_hot_good_out[y][4]
clasF = one_hot_good_out[y][5]
clasF = one_hot_good_out[y][6]
#bruhOne = []
if True:
zero = 1
toPassOne.append(zero)
one = 0
toPassOne.append(one)
two = 0
toPassOne.append(two)
three = 0
toPassOne.append(three)
else:
print "not here"
#toPassOne.append(bruhOne)
#print bruhOne
#print "TO PASS"
#print toPassOne
toPassTwo = []
#for x in range(10):
y = windowInd
if y < len(one_hot_bad_ins_out):
err = one_hot_bad_ins_out[y][0]
clasF = one_hot_bad_ins_out[y][4]
clasF = one_hot_bad_ins_out[y][5]
clasF = one_hot_bad_ins_out[y][6]
#bruhTwo = []
if True:
zero = 0
toPassTwo.append(zero)
one = 0
toPassTwo.append(one)
two = 0
toPassTwo.append(two)
three = 1
toPassTwo.append(three)
else:
print "GET OUT OF HERE"
print type(radha)
#toPassTwo.append(bruhTwo)
toPassThree = []
#for x in range(10):
y = windowInd
if y < len(one_hot_bad_del_out):
#toPassThree.append(one_hot_bad_del_out[y])
err = one_hot_bad_del_out[y][0]
clasF = one_hot_bad_del_out[y][4]
clasF = one_hot_bad_del_out[y][5]
clasF = one_hot_bad_del_out[y][6]
#bruhThree = []
if True:
zero = 0
toPassThree.append(zero)
one = 0
toPassThree.append(one)
two = 1
toPassThree.append(two)
three = 0
toPassThree.append(three)
else:
print "GET OUT OF HERE"
print type(radha)
#toPassThree.append(bruhThree)
toPassFour = []
#for x in range(10):
y = windowInd
if y < len(one_hot_bad_sub_out):
#toPassFour.append(one_hot_bad_sub_out[y])
err = one_hot_bad_sub_out[y][0]
clasF = one_hot_bad_sub_out[y][4]
clasF = one_hot_bad_sub_out[y][5]
clasF = one_hot_bad_sub_out[y][6]
#bruhFour = []
if True:
zero = 0
toPassFour.append(zero)
one = 1
toPassFour.append(one)
two = 0
toPassFour.append(two)
three = 0
toPassFour.append(three)
else:
zero = 1
toPassFour.append(zero)
one = 0
toPassFour.append(one)
two = 0
toPassFour.append(two)
three = 0
toPassFour.append(three)
#toPassFour.append(bruhFour)
#print "TEST OUT"
#print toPassOne[0]
#print len(toPass)
#toPass = np.array((toPassOne, toPassTwo, toPassThree, toPassFour))
#print toPass.shape
while(batchInd % 4 != 0):
toPass = []
#print "BATCH IND"
#print batchInd
if(batchInd == 1):
toPass = toPassOne[:]
elif(batchInd == 2):
toPass = toPassTwo[:]
elif(batchInd == 3):
toPass = toPassThree[:]
elif(batchInd == 4):
print "here"
toPass = toPassTwo[:]
#print len(toPass)
#print toPassOne[1]
a = numpy.array(toPass)
#a = b[None, :]
#print a.shape
#print numpy.array(toPassOne)
#print a
#count+=1
#print "COUNT"
#print count
#print b.shape
#print a
#yield a
batchArr.append(a)
batchInd += 1
#toPass = np.concatenate((numpy.array(toPassOne).astype(int), numpy.array(toPassTwo).astype(int), numpy.array(toPassThree).astype(int)), axis=0)
#a = numpy.array(toPass)
#yield a
windowInd += 1
#print loopInd
loopInd += 1
else:
#print "NEXT FILE"
#old_one_hot_good_out = one_hot_good_out[:]
#old_one_hot_bad_ins_out = one_hot_bad_ins_out[:]
#old_one_hot_bad_del_out = one_hot_bad_del_out[:]
#old_one_hot_bad_sub_out = one_hot_bad_sub_out[:]
#numGoodOutLeft = len(one_hot_good_out) % 10
#numBadInsOutLeft = len(one_hot_bad_ins_out) % 10
#numBadDelOutLeft = len(one_hot_bad_del_out) % 10
#numBadSubOutLeft = len(one_hot_bad_sub_out) % 10
fileInd += 1
if fileInd >= 1000:
fileInd = 0
windowInd = 0
#_, _, _, _, one_hot_good_out, one_hot_bad_ins_out, one_hot_bad_del_out, one_hot_bad_sub_out, _ = perform(fileInd)
print fileInd
#print allValData[37]
#print type(allValData[fileInd][1])
#print type(allValData[fileInd][2])
#print type(allValData[fileInd][3])
one_hot_good_out = allValData[fileInd]
while(one_hot_good_out == -1):
fileInd+=1
if fileInd >= 1000:
fileInd = 0
#_, _, _, _, one_hot_good_out, one_hot_bad_ins_out, one_hot_bad_del_out, one_hot_bad_sub_out, _ = perform(fileInd)
one_hot_good_out, one_hot_bad_ins_out, one_hot_bad_del_out, one_hot_bad_sub_out = one_hot(allValData[fileInd][0]),one_hot(allValData[fileInd][1]), one_hot(allValData[fileInd][2]), one_hot(allValData[fileInd][3])
if one_hot_good_out != -1:
one_hot_good_out, one_hot_bad_ins_out, one_hot_bad_del_out, one_hot_bad_sub_out = one_hot(allValData[fileInd][0]),one_hot(allValData[fileInd][1]), one_hot(allValData[fileInd][2]), one_hot(allValData[fileInd][3])
#for p in range(numGoodOutLeft):
# one_hot_good_out.insert(p, old_one_hot_good_out[len(old_one_hot_good_out)-numGoodOutLeft+p])
#for p in range(numBadInsOutLeft):
# one_hot_bad_ins_out.insert(p, old_one_hot_bad_ins_out[len(old_one_hot_bad_ins_out)-numBadInsOutLeft+p])
#for p in range(numBadDelOutLeft):
# one_hot_bad_del_out.insert(p, old_one_hot_bad_del_out[len(old_one_hot_bad_del_out)-numBadDelOutLeft+p])
#for p in range(numBadSubOutLeft):
# one_hot_bad_sub_out.insert(p, old_one_hot_bad_sub_out[len(old_one_hot_bad_sub_out)-numBadSubOutLeft+p])
#print loopInd
#loopInd += 1
#print numpy.array(batchArr).shape
#print len(batchArr)
#print "dhvani"
b = numpy.array(batchArr)
#print b.shape
yield b
def getInputTestTen():
one_hot_good, one_hot_bad_ins, one_hot_bad_del, one_hot_bad_sub, _, _, _, _, passInsErrorInd = perform(2037)
fileInd = 2037
batchInd = 1
if True:
if True:
#print passInsErrorInd
print "ERROR IND"
print one_hot_good
toPassOne = []
for x in range(10):
y = x
if y < len(one_hot_good):
toPassOne.append(one_hot_good[y])
toPassTwo = []
for x in range(10):
y = passInsErrorInd - x
if y < len(one_hot_bad_ins):
toPassTwo.append(one_hot_bad_ins[y])
toPassThree = []
for x in range(10):
y = x
if y < len(one_hot_bad_del):
toPassThree.append(one_hot_bad_del[y])
toPassFour = []
for x in range(10):
y = x
if y < len(one_hot_bad_sub):
toPassFour.append(one_hot_bad_sub[y])
#print len(toPass)
#print len(toPassOne)
#print len(toPassTwo)
#print len(toPassThree)
#print len(toPassFour)
#toPass = np.array((toPassOne, toPassTwo, toPassThree, toPassFour))
#print toPass.shape
toPass = toPassTwo[:]
a = numpy.array(toPass).astype(int)
#print b.shape
return a
#print numpy.array(toPass).shape
#print "mine too"
batchInd += 1
windowInd += 1
else:
#print "NEXT FILE"
old_one_hot_good = one_hot_good[:]
old_one_hot_bad_ins = one_hot_bad_ins[:]
old_one_hot_bad_del = one_hot_bad_del[:]
old_one_hot_bad_sub = one_hot_bad_sub[:]
numGoodLeft = len(one_hot_good) % 10
numBadInsLeft = len(one_hot_bad_ins) % 10
numBadDelLeft = len(one_hot_bad_del) % 10
numBadSubLeft = len(one_hot_bad_sub) % 10
fileInd += 1
windowInd = 0
one_hot_good, one_hot_bad_ins, one_hot_bad_del, one_hot_bad_sub, _, _, _, _ = perform(fileInd)
while(one_hot_good == None):
fileInd+=1
one_hot_good, one_hot_bad_ins, one_hot_bad_del, one_hot_bad_sub, _, _, _, _ = perform(fileInd)
for p in range(numGoodLeft):
one_hot_good.insert(p, old_one_hot_good[len(old_one_hot_good)-numGoodLeft+p])
for p in range(numBadInsLeft):
one_hot_bad_ins.insert(p, old_one_hot_bad_ins[len(old_one_hot_bad_ins)-numBadInsLeft+p])
for p in range(numBadDelLeft):
one_hot_bad_del.insert(p, old_one_hot_bad_del[len(old_one_hot_bad_del)-numBadDelLeft+p])
for p in range(numBadSubLeft):
one_hot_bad_sub.insert(p, old_one_hot_bad_sub[len(old_one_hot_bad_sub)-numBadSubLeft+p])
def getOutputTestTen():
_, _, _, _, one_hot_good_out, one_hot_bad_ins_out, one_hot_bad_del_out, one_hot_bad_sub_out = perform(10)
windowInd = 0
fileInd = 10
batchInd = 1
while fileInd <= 11: # 462540
#while windowInd < int(len(insArr)/10):
sizes = [len(one_hot_good_out), len(one_hot_bad_ins_out),len(one_hot_bad_del_out),len(one_hot_bad_sub_out)]
minSize = min(float(siz) for siz in sizes) # min of a generator
if windowInd < int(minSize/10):
toPassOne = []
for x in range(10):
y = x + windowInd
if y < len(one_hot_good_out):
toPassOne.append(one_hot_good_out[y])
toPassTwo = []
for x in range(10):
y = x + windowInd
if y < len(one_hot_bad_ins_out):
toPassTwo.append(one_hot_bad_ins_out[y])
toPassThree = []
for x in range(10):
y = x + windowInd
if y < len(one_hot_bad_del_out):
toPassThree.append(one_hot_bad_del_out[y])
toPassFour = []
for x in range(10):
y = x + windowInd
if y < len(one_hot_bad_sub_out):
toPassFour.append(one_hot_bad_sub_out[y])
#print len(toPass)
#toPass = np.array((toPassOne, toPassTwo, toPassThree, toPassFour))
#print toPass.shape
toPass = []
if(batchInd == 1):
toPass = toPassOne[:]
elif(batchInd == 2):
toPass = toPassTwo[:]
elif(batchInd == 3):
toPass = toPassThree[:]
elif(batchInd == 4):
toPass = toPassFour[:]
if(batchInd % 4 == 0):
batchInd = 1
a = numpy.array(toPass).astype(int)
#print b.shape
yield a
#print numpy.array(toPass).shape
#print "mine"
batchInd += 1
windowInd += 1
else:
#print "NEXT FILE"
old_one_hot_good_out = one_hot_good_out[:]
old_one_hot_bad_ins_out = one_hot_bad_ins_out[:]
old_one_hot_bad_del_out = one_hot_bad_del_out[:]
old_one_hot_bad_sub_out = one_hot_bad_sub_out[:]
numGoodOutLeft = len(one_hot_good_out) % 10
numBadInsOutLeft = len(one_hot_bad_ins_out) % 10
numBadDelOutLeft = len(one_hot_bad_del_out) % 10
numBadSubOutLeft = len(one_hot_bad_sub_out) % 10
fileInd += 1
windowInd = 0
_, _, _, _, one_hot_good_out, one_hot_bad_ins_out, one_hot_bad_del_out, one_hot_bad_sub_out = perform(fileInd)
while(one_hot_good_out == None):
fileInd+=1
_, _, _, _, one_hot_good_out, one_hot_bad_ins_out, one_hot_bad_del_out, one_hot_bad_sub_out = perform(fileInd)