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OverlapChunkTest2.py
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from keras.utils import to_categorical
import numpy as np
import math
import matplotlib.pyplot as plt
class OverlapChunkTest2:
def __init__(self, time_delay):
self.chunk= 0
self.output_size = 10
self.counter = -1
self.time_delay = time_delay
self.time_counter = time_delay
self.output_class= 0
self.previous_output_class= None
self.sequenceA_length = 4
self.sequenceB_length = 4 #np.random.randint(2)+5
def getOutputSize(self):
return self.output_size
def trueLabel(self):
truelabel= np.array((0,0,0,0,0,1,1,1,1,1))
return truelabel
def updateTimeDelay(self):
self.time_counter+= 1
if self.time_counter > self.time_delay:
self.time_counter = 0
return True
else:
return False
#create an input pattern for the system
def getInput(self, reset = False):
if reset == True:
self.chunk=0
self.counter=-1
update = self.updateTimeDelay()
if update == True:
if self.chunk == 0:
if self.counter > self.sequenceA_length:
self.chunk = 1
self.counter= 0
else:
self.counter+= 1
else:
if self.counter > self.sequenceB_length:
#self.sequenceB_length = np.random.randint(20)+5
self.chunk = 0
self.counter= 0
else:
self.counter+= 1
if self.chunk == 0:
#input_value = np.random.randint(10)
#input_value= self.counter
self.previous_output_class= self.output_class
#possible outputs are 0,1,2,3,4
self.output_class = np.random.randint(5)
else:
self.previous_output_class= self.output_class
#possible outputs are 0,1,2,3,4,5,6,7,8,9
self.output_class = np.random.randint(10)
noise_intensity= 0
# input_value = to_categorical(self.output_class, self.output_size) + np.random.randn(self.output_size)*noise_intensity
if self.previous_output_class is None or self.previous_output_class == self.output_class:
input_value = to_categorical(self.output_class, self.output_size)*np.exp(-0.1*self.time_counter) + np.random.randn(self.output_size)*noise_intensity
else:
input_value = to_categorical(self.output_class, self.output_size)*np.exp(-0.1*self.time_counter) + np.random.randn(self.output_size)*noise_intensity + to_categorical(self.previous_output_class, self.output_size)*np.exp(-0.1*(self.time_counter+self.time_delay))
return input_value
def getOutputSize(self):
return self.output_size
def getSequence(self, iterations):
input_class = np.empty(iterations)
input_sequence = np.empty((iterations, self.output_size))
for i in range(iterations):
input_value = self.getInput()
#input_class.append(self.chunk)
#input_sequence.append(input_value)
input_class[i] = self.chunk
input_sequence[i] = input_value
return input_sequence, input_class
def plot(self, input_class, input_sequence = None, save = False):
a = np.asarray(input_class)
t = [i for i,value in enumerate(a)]
plt.plot(t, a)
if input_sequence != None:
sequence = [np.argmax(x) for x in input_sequence]
plt.plot(t, sequence)
if save == True:
plt.savefig("plot.png")
plt.show()
plt.close()
def plotSuperposed(self, input_class, input_sequence = None, save = False):
input_sequence= np.asarray(input_sequence)
t = [i for i,value in enumerate(input_sequence)]
print(input_sequence.shape)
#exit()
for i in range(input_sequence.shape[1]):
a = input_sequence[:,i]
plt.plot(t, a)
a = np.asarray(input_class)
plt.plot(t, a)
if save == True:
plt.savefig("plot.png")
plt.show()
plt.close()