def __init__(self, sp_trainer):
"""
Parameters:
----------
sp_trainer : The spatial pooler trainer
"""
self.sp_trainer = sp_trainer
self.input_array = np.zeros(self.sp_trainer.fp_length, dtype="int32")
self.active_array = np.zeros(self.sp_trainer.num_columns,
dtype="int32")
self.is_learning = True
self.compute_inference = False
self.tp = TP(numberOfCols=self.sp_trainer.num_columns,
cellsPerColumn=2,
initialPerm=0.5,
connectedPerm=0.5,
minThreshold=10,
newSynapseCount=10,
permanenceInc=0.1,
permanenceDec=0.0,
activationThreshold=5,
globalDecay=0,
burnIn=1,
checkSynapseConsistency=False,
pamLength=10)
def __init__(self,
numberOfCols=16384,
cellsPerColumn=8,
initialPerm=0.5,
connectedPerm=0.5,
minThreshold=164,
newSynapseCount=164,
permanenceInc=0.1,
permanenceDec=0.0,
activationThreshold=164,
pamLength=10,
checkpointDir=None):
self.tp = TP(
numberOfCols=numberOfCols,
cellsPerColumn=cellsPerColumn,
initialPerm=initialPerm,
connectedPerm=connectedPerm,
minThreshold=minThreshold,
newSynapseCount=newSynapseCount,
permanenceInc=permanenceInc,
permanenceDec=permanenceDec,
# 1/2 of the on bits = (16384 * .02) / 2
activationThreshold=activationThreshold,
globalDecay=0,
burnIn=1,
checkSynapseConsistency=False,
pamLength=pamLength)
self.checkpointDir = checkpointDir
self.checkpointPath = None
self._initCheckpoint()
def run():
tp = TP(numberOfCols=121,
cellsPerColumn=4,
initialPerm=0.5,
connectedPerm=0.5,
minThreshold=11,
newSynapseCount=11,
permanenceInc=0.1,
permanenceDec=0.05,
activationThreshold=2,
globalDecay=0,
burnIn=1,
checkSynapseConsistency=False,
pamLength=3)
Patcher().patchTP(tp)
inputArray = numpy.zeros(tp.numberOfCols, dtype='int32')
for i in range(100):
generateInput(inputArray)
tp.compute(inputArray, enableLearn=True, computeInfOutput=True)
print "Ran iteration:\t{0}".format(i)
def __init__(self):
"""
Instantiate temporal pooler, encoder, audio sampler, filter, & freq plot
"""
self.vis = Visualizations()
"""
The number of columns in the input and therefore the TP
2**9 = 512
Trial and error pulled that out
numCols should be tested during benchmarking
"""
self.numCols = 2**9
sparsity = 0.10
self.numInput = int(self.numCols * sparsity)
"""
Create a bit map encoder
From the encoder's __init__ method:
1st arg: the total bits in input
2nd arg: the number of bits used to encode each input bit
"""
self.e = SparsePassThroughEncoder(self.numCols, 1)
"""
Sampling details
rate: The sampling rate in Hz of my soundcard
buffersize: The size of the array to which we will save audio segments (2^12 = 4096 is very good)
secToRecord: The length of each sampling
buffersToRecord: how many multiples of buffers are we recording?
"""
rate = 44100
secToRecord = .1
self.buffersize = 2**12
self.buffersToRecord = int(rate * secToRecord / self.buffersize)
if not self.buffersToRecord:
self.buffersToRecord = 1
"""
Filters in Hertz
highHertz: lower limit of the bandpass filter, in Hertz
lowHertz: upper limit of the bandpass filter, in Hertz
max lowHertz = (buffersize / 2 - 1) * rate / buffersize
"""
highHertz = 500
lowHertz = 10000
"""
Convert filters from Hertz to bins
highpass: convert the highHertz into a bin for the FFT
lowpass: convert the lowHertz into a bin for the FFt
NOTES:
highpass is at least the 1st bin since most mics only pick up >=20Hz
lowpass is no higher than buffersize/2 - 1 (highest array index)
passband needs to be wider than size of numInput - not checking for that
"""
self.highpass = max(int(highHertz * self.buffersize / rate), 1)
self.lowpass = min(int(lowHertz * self.buffersize / rate),
self.buffersize / 2 - 1)
"""
The call to create the temporal pooler region
"""
self.tp = TP(numberOfCols=self.numCols,
cellsPerColumn=4,
initialPerm=0.5,
connectedPerm=0.5,
minThreshold=10,
newSynapseCount=10,
permanenceInc=0.1,
permanenceDec=0.07,
activationThreshold=8,
globalDecay=0.02,
burnIn=2,
checkSynapseConsistency=False,
pamLength=100)
"""
Creating the audio stream from our mic
"""
p = pyaudio.PyAudio()
#self.inStream = p.open(format=pyaudio.paInt32,channels=1,rate=rate,input=True,frames_per_buffer=self.buffersize)
self.inStream = p.open(format=pyaudio.paInt32,
channels=1,
rate=rate,
input=True,
input_device_index=4,
frames_per_buffer=self.buffersize)
#参考 成功したpyaudio の設定
#stream = audio.open(format=pyaudio.paInt16, channels=CHANNELS,rate=RATE, input=True,input_device_index=4,frames_per_buffer=CHUNK)
"""
Setting up the array that will handle the timeseries of audio data from our input
"""
self.audio = numpy.empty((self.buffersToRecord * self.buffersize),
dtype="uint32")
"""
Print out the inputs
"""
print "Number of columns:\t" + str(self.numCols)
print "Max size of input:\t" + str(self.numInput)
print "Sampling rate (Hz):\t" + str(rate)
print "Passband filter (Hz):\t" + str(highHertz) + " - " + str(
lowHertz)
print "Passband filter (bin):\t" + str(self.highpass) + " - " + str(
self.lowpass)
print "Bin difference:\t\t" + str(self.lowpass - self.highpass)
print "Buffersize:\t\t" + str(self.buffersize)
"""
Setup the plot
Use the bandpass filter frequency range as the x-axis
Rescale the y-axis
"""
plt.ion()
bin = range(self.highpass, self.lowpass)
xs = numpy.arange(len(bin)) * rate / self.buffersize + highHertz
self.freqPlot = plt.plot(xs, xs)[0]
plt.ylim(0, 10**12)
while True:
self.processAudio()
s += ' '
s += str(x[c])
s += ' '
return s
#######################################################################
#
# Step 1: create Temporal Pooler instance with appropriate parameters
tp = TP(numberOfCols=50,
cellsPerColumn=1,
initialPerm=0.5,
connectedPerm=0.5,
minThreshold=10,
newSynapseCount=10,
permanenceInc=0.1,
permanenceDec=0.0,
activationThreshold=8,
globalDecay=0,
burnIn=1,
checkSynapseConsistency=False,
pamLength=10)
#######################################################################
#
# Step 2: create input vectors to feed to the temporal pooler. Each input vector
# must be numberOfCols wide. Here we create a simple sequence of 5 vectors
# representing the sequence A -> B -> C -> D -> E
x = numpy.zeros((5, tp.numberOfCols), dtype="uint32")
x[0, 0:10] = 1 # Input SDR representing "A"
x[1, 10:20] = 1 # Input SDR representing "B"
flatInput = input.flatten()
#print "flatInput = ", flatInput
iterationOutput = numpy.zeros(shape=flatInputLength, dtype="uint8")
spatialPooler.compute(inputVector=flatInput,
learn=True,
activeArray=iterationOutput)
print "Iteration " + str(i) + ":", iterationOutput
print "Initializing temporal pooler"
temporalPooler = TP(
numberOfCols=flatInputLength,
cellsPerColumn=104, # c++ version max = 104
initialPerm=0.5,
connectedPerm=0.5,
minThreshold=10,
newSynapseCount=10,
permanenceInc=0.1,
permanenceDec=0.0,
activationThreshold=1,
globalDecay=0,
burnIn=1,
checkSynapseConsistency=False,
pamLength=1)
print "temporal pooler initiaization complete\n"
## train temporal pooler with all potential spatial pooler outputs
#print "training temporal pooler"
#for x in xrange(0, inputWidth * inputHeight):
# trainingData = numpy.zeros(shape = flatInputLength, dtype = "int32")
# trainingData[x] = 1
# temporalPooler.compute(bottomUpInput = trainingData, enableLearn = True, computeInfOutput = False)
#print "training temporal pooler complete\n"
__author__ = 'chandanmaruthi'
from nupic.research.TP10X2 import TP
import numpy
import os
import cPickle as pickle
# Step 1: create Temporal Pooler instance with appropriate parameters
tp = TP(numberOfCols=50, cellsPerColumn=5,
initialPerm=0.5, connectedPerm=0.5,
minThreshold=10, newSynapseCount=10,
permanenceInc=0.1, permanenceDec=0.0,
activationThreshold=8,
globalDecay=0, burnIn=1,
checkSynapseConsistency=False,
pamLength=10)
path = os.path.abspath("/home/chandanmaruthi/chandan/code/brainscience/tptest.p")
# Step 2: create input vectors to feed to the temporal pooler. Each input vector
# must be numberOfCols wide. Here we create a simple sequence of 5 vectors
# representing the sequence A -> B -> C -> D -> E
x = numpy.zeros((5, tp.numberOfCols), dtype="uint32")
x[0,0:10] = 1 # Input SDR representing "A", corresponding to columns 0-9
x[1,10:20] = 1 # Input SDR representing "B", corresponding to columns 10-19
x[2,20:30] = 1 # Input SDR representing "C", corresponding to columns 20-29
x[3,30:40] = 1 # Input SDR representing "D", corresponding to columns 30-39
x[4,40:50] = 1 # Input SDR representing "E", corresponding to columns 40-49
# Step 3: send this simple sequence to the temporal pooler for learning
# We repeat the sequence 10 times
test=1