class CLAModel(object):
def __init__(self):
self.dim = 64**2
self.tp = TP(numberOfCols=64**2, cellsPerColumn=32,
initialPerm=0.5, connectedPerm=0.5,
minThreshold=10, newSynapseCount=40,
permanenceInc=0.1, permanenceDec=0.00001,
activationThreshold=int((64**2 * .02) / 2),
globalDecay=0, burnIn=1,
checkSynapseConsistency=False,
pamLength=10)
def train(self, sdr, learning):
values = self.toArray(sdr, self.dim)
self.tp.compute(values, learning, computeInfOutput=True)
predictedCells = self.tp.getPredictedState()
predictedColumns = predictedCells.max(axis=1)
predictedBitmap = predictedColumns.nonzero()[0]
return list(predictedBitmap)
def toArray(self, lst, length):
res = np.zeros(length, dtype="uint32")
for val in lst:
res[val] = 1
return res
class TPTrainer():
"""Trainer for the temporal pooler.
Takes word fingerprints from an input file and feeds them to the
temporal pooler by first getting the SDR from the spatial pooler
and then passing that to the temporal pooler.
"""
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 run(self, fp):
"""Run the spatial pooler and temporal pooler with the input fingerprint"""
# clear the input_array to zero before creating a new input vector
self.input_array[0:] = 0
self.input_array[list(fp)] = 1
# active_array[column] = 1 if column is active after spatial pooling
self.sp_trainer.sp.compute(self.input_array, False, self.active_array)
self.tp.compute(self.active_array,
enableLearn=self.is_learning,
computeInfOutput=self.compute_inference)
if self.compute_inference:
self.predicted_sdr = set(
self.tp.getPredictedState().max(axis=1).nonzero()[0].flat)
lemma_sdrs = np.array(
[l for l in self.sp_trainer.lemma_to_sdr.values()])
# convert string key to list by stripping front and end:
# Example: (array([ 8, 12, ... , 1018]),)
all_lemma_sdrs = [
set(eval(l.last_sdr_key[7:-3])) for l in lemma_sdrs
]
_, indexes = wordnet_fp.find_matching(self.predicted_sdr,
all_lemma_sdrs, 1, 10)
self.predicted_lemmas = lemma_sdrs[indexes]
def main(SEED):
# input 生成
numOnBitsPerPattern = 3
(numCols, trainingSequences) = buildOverlappedSequences(
numSequences = 2, # 生成するsequenceの数
seqLen = 5, # sequenceの長さ
sharedElements = [2,3], # 異なるsequence間で同じものが含まれている番号
numOnBitsPerPattern = 3, # activeになるカラム数
patternOverlap = 0 # activeになるカラムが重なっている数
)
print numCols
for sequence in trainingSequences:
print sequence
# TP生成
tp = TP(
numberOfCols = numCols,
cellsPerColumn = 2,
initialPerm = 0.6,
connectedPerm = 0.5,
minThreshold = 3,
newSynapseCount = 3,
permanenceInc = 0.1,
permanenceDec = 0.0,
activationThreshold = 3,
globalDecay = 0.0,
burnIn = 1,
seed = SEED,
verbosity = 0,
checkSynapseConsistency = True,
pamLength = 1
)
# TP学習
for _ in range(10):
for seq_num, sequence in enumerate(trainingSequences):
for x in sequence:
x = numpy.array(x).astype('float32')
tp.compute(x, enableLearn = True, computeInfOutput=True)
#tp.printStates(False, False)
tp.reset()
# TP 予測
for seq_num, sequence in enumerate(trainingSequences):
for x in sequence:
x = numpy.array(x).astype('float32')
tp.compute(x, enableLearn = False, computeInfOutput = True)
tp.printStates(False, False)
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 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)
class AudioStream:
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()
def processAudio(self):
"""
Sample audio, encode, send it to the TP
Pulls the audio from the mic
Conditions that audio as an SDR
Computes a prediction via the TP
Update the visualizations
"""
"""
Cycle through the multiples of the buffers we're sampling
Sample audio to store for each frame in buffersize
Mic voltage-level timeseries is saved as 32-bit binary
Convert that 32-bit binary into integers, and save to array for the FFT
"""
for i in range(self.buffersToRecord):
try:
audioString = self.inStream.read(self.buffersize)
except IOError:
print "Overflow error from 'audiostring = inStream.read(buffersize)'. Try decreasing buffersize."
quit()
self.audio[i * self.buffersize:(i + 1) *
self.buffersize] = numpy.fromstring(audioString,
dtype="uint32")
"""
Get int array of strength for each bin of frequencies via fast fourier transform
Get the indices of the strongest frequencies (the top 'numInput')
Scale the indices so that the frequencies fit to within numCols
Pick out the unique indices (we've reduced the mapping, so we likely have multiples)
Encode those indices into an SDR via the SparsePassThroughEncoder
Cast the SDR as a float for the TP
"""
ys = self.fft(self.audio, self.highpass, self.lowpass) #fft の結果
fs = numpy.sort(
ys.argsort()[-self.numInput:]) #ysを昇順にソートして結果の上位から入力数だけのインデックス
ufs = fs.astype(numpy.float32) / (
self.lowpass -
self.highpass) * self.numCols #fsをフロートに変換して、バンド幅で割り、カラム数で割る。
ufs = ufs.astype(numpy.int32)
ufs = numpy.unique(ufs) #重複をなくす
actual = numpy.zeros(self.numCols, dtype=numpy.int32)
for index in ufs:
actual += self.e.encode(index)
actualInt = actual
"""
Pass the SDR to the TP
Collect the prediction SDR from the TP
Pass the prediction & actual SDRS to the anomaly calculator & array comparer
Update the frequency plot
"""
self.tp.compute(actual, enableLearn=True, computeInfOutput=True)
predictedInt = self.tp.getPredictedState().max(axis=1)
compare = self.vis.compareArray(actualInt, predictedInt)
anomaly = self.vis.calcAnomaly(actualInt, predictedInt)
print ".".join(compare)
print self.vis.hashtagAnomaly(anomaly)
self.freqPlot.set_ydata(ys)
#plt.plot(self.xs,ys)
plt.show(block=False)
plt.draw()
def fft(self, audio, highpass, lowpass):
"""
Fast fourier transform conditioning
Output:
'output' contains the strength of each frequency in the audio signal
frequencies are marked by its position in 'output':
frequency = index * rate / buffesize
output.size = buffersize/2
Method:
Use numpy's FFT (numpy.fft.fft)
Find the magnitude of the complex numbers returned (abs value)
Split the FFT array in half, because we have mirror frequencies
(they're the complex conjugates)
Use just the first half to apply the bandpass filter
Great info here: http://stackoverflow.com/questions/4364823/how-to-get-frequency-from-fft-result
"""
left, right = numpy.split(numpy.abs(numpy.fft.fft(audio)), 2)
output = left[highpass:lowpass]
return output
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
for i in range(10):
# Send each letter in the sequence in order
for j in range(5):
# The compute method performs one step of learning and/or inference. Note:
# here we just perform learning but you can perform prediction/inference and
# learning in the same step if you want (online learning).
tp.compute(x[j], enableLearn = True, computeInfOutput = False)
# This function prints the segments associated with every cell.
# If you really want to understand the TP, uncomment this line. By following
# every step you can get an excellent understanding for exactly how the TP
# learns.
#tp.printCells()
# The reset command tells the TP that a sequence just ended and essentially
# zeros out all the states. It is not strictly necessary but it's a bit
# messier without resets, and the TP learns quicker with resets.
tp.reset()
#######################################################################
#
x[3, 30:40] = 1 # Input SDR representing "D"
x[4, 40:50] = 1 # Input SDR representing "E"
#######################################################################
#
# Step 3: send this simple sequence to the temporal pooler for learning
# We repeat the sequence 10 times
for i in range(10):
# Send each letter in the sequence in order
for j in range(5):
# The compute method performs one step of learning and/or inference. Note:
# here we just perform learning but you can perform prediction/inference and
# learning in the same step if you want (online learning).
tp.compute(x[j], enableLearn=True, computeInfOutput=False)
# The reset command tells the TP that a sequence just ended and essentially
# zeros out all the states. It is not strictly necessary but it's a bit
# messier without resets, and the TP learns quicker with resets.
tp.reset()
#######################################################################
#
# Step 3: send the same sequence of vectors and look at predictions made by
# temporal pooler
for j in range(5):
print "\n\n--------", "ABCDE"[j], "-----------"
print "Raw input vector\n", formatRow(x[j])
# Send each vector to the TP, with learning turned off
class AudioStream:
def printWaveInfo(self, wf):
"""
WAVEファイルの情報を取得
"""
print()
print("チャンネル数:", wf.getnchannels() )
print("サンプル幅:", wf.getsampwidth() )
print("サンプリング周波数:", wf.getframerate() )
print("フレーム数:", wf.getnframes() )
print("パラメータ:", wf.getparams() )
print("長さ（秒）:", float(wf.getnframes()) / wf.getframerate() )
print()
def __init__(self, wf=None):
"""
wf = None : mic
"""
# Visualizations of result
self.vis = Visualizations()
# network parameter
self.numCols = 2**9 # 2**9 = 512
sparsity = 0.10
self.numInput = int(self.numCols * sparsity)
# encoder of audiostream
self.e = BitmapArrayEncoder(self.numCols, 1)
# setting audio
p = pyaudio.PyAudio()
if wf == None:
self.wf = None
channels = 1
rate = 44100 # sampling周波数: １秒間に44100回
secToRecord = .1 #
self.buffersize = 2**12
self.buffersToRecord=int(rate*secToRecord/self.buffersize)
if not self.buffersToRecord:
self.buffersToRecord = 1
audio_format = pyaudio.paInt32
else:
self.printWaveInfo(wf)
channels = wf.getnchannels()
self.wf = wf
rate = wf.getframerate()
secToRecord = wf.getsampwidth()
self.buffersize = 1024
self.buffersToRecord=int(rate*secToRecord/self.buffersize)
if not self.buffersToRecord:
self.buffersToRecord = 1
audio_format = p.get_format_from_width(secToRecord)
self.inStream = p.open(
format=audio_format,
channels=channels,
rate=rate,
input=True,
output=True,
frames_per_buffer=self.buffersize)
self.audio = numpy.empty((self.buffersToRecord*self.buffersize), dtype="uint32")
# filters in Hertz
# max lowHertz = (buffersize / 2-1) * rate / buffersize
highHertz = 500
lowHertz = 10000
# Convert filters from Hertz to bins
self.highpass = max(int(highHertz * self.buffersize /rate), 1)
self.lowpass = min(int(lowHertz * self.buffersize / rate), self.buffersize/2 -1)
# Temporal Pooler
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
)
print("Number of columns: ", str(self.numCols))
print("Max size of input: ", str(self.numInput))
print("Sampling rate(Hz): ", str(rate))
print("Passband filter(Hz): ", str(highHertz), " - ", str(lowHertz))
print("Passband filter(bin):", str(self.highpass), " - ", str(self.lowpass))
print("Bin difference: ", str(self.lowpass - self.highpass))
print("Buffersize: ", str(self.buffersize))
# # setup plot
# 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)
def plotPerformance(self, values, window=1000):
plt.clf()
plt.plot(values[-window:])
plt.gcf().canvas.draw()
# Without the next line, the pyplot plot won't actually show up.
plt.pause(0.001)
def playAudio(self):
"""
指定されているwaveを再生
同時に波形をplot
"""
chunk = 22050 # 音源が0.5秒毎に切り替わっていたため.
data = self.wf.readframes(chunk)
#plt.ion()
#data_list = []
predictedInt = None
plt.figure(figsize=(15, 5))
while data != '':
dat = numpy.fromstring(data, dtype = "uint32")
#print(dat.shape, dat)
# plot
data_list = dat.tolist()
self.plotPerformance(data_list, window=500)
# plt.plot(dat)
# plt.show(block = False)
# plt.draw()
# 音ならす.
self.inStream.write(data)
# sampling値 -> SDR
actualInt, actual = self.encoder(data_list)
# actualInt, predictedInt 比較
if not predictedInt == None:
compare = self.vis.compareArray(actualInt, predictedInt)
print("." . join(compare) )
anomaly = self.vis.calcAnomaly(actualInt, predictedInt)
print(self.vis.hashtagAnomaly(anomaly) )
# TP predict
predictedInt = self.tp_learn_and_predict(actual)
# 次のデータ
data = self.wf.readframes(chunk)
self.inStream.close()
p.terminate()
def tp_learn_and_predict(self, data):
self.tp.compute(data, enableLearn = True, computeInfOutput = True)
predictedInt = self.tp.getPredictedState().max(axis=1)
return predictedInt
def encoder(self, data):
# sampling 値 -> 周波数成分
ys = self.fft(data, self.highpass, self.lowpass)
# 1. 強い周波数成分の上位numInputのindexを取得する.
# 2. 数字のレンジをnumColsに合わせる.
# 3. uniqにする. (いるの?)
fs = numpy.sort(ys.argsort()[-self.numInput:])
rfs = fs.astype(numpy.float32) / (self.lowpass - self.highpass) * self.numCols
ufs = numpy.unique(rfs)
# encode
actualInt = self.e.encode(ufs)
actual = actualInt.astype(numpy.float32)
return actualInt, actual
def fft(self, audio, highpass, lowpass):
left, right = numpy.split(numpy.abs(numpy.fft.fft(audio)), 2)
output = left[highpass:lowpass]
return output
def formatRow(self, x):
s = ''
for c in range(len(x)):
if c > 0 and c % 10 == 0:
s += ' '
s += str(x[c])
s += ' '
return s
def getAudioString(self):
if self.wf == None:
print(self.buffersToRecord)
for i in range(self.buffersToRecord):
try:
audioString = self.inStream.read(self.buffersize)
except IOError:
print("Overflow error from 'audiostring = inStream.read(buffersize)'. Try decreasing buffersize.")
quit()
self.audio[i*self.buffersize:(i+1)*self.buffersize] = numpy.fromstring(audioString, dtype = "uint32")
else:
for i in range(self.buffersToRecord):
audioString = self.wf.readframes(self.buffersize)
self.audio[i*self.buffersize:(i+1)*self.buffersize] = numpy.fromstring(audioString, dtype = "uint32")
def plotWave(self):
self.getAudioString()
print(self.audio)
plt.plot(audiostream.audio[0:1000])
plt.show()
class AudioStream:
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)
"""
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()
def processAudio (self):
"""
Sample audio, encode, send it to the TP
Pulls the audio from the mic
Conditions that audio as an SDR
Computes a prediction via the TP
Update the visualizations
"""
"""
Cycle through the multiples of the buffers we're sampling
Sample audio to store for each frame in buffersize
Mic voltage-level timeseries is saved as 32-bit binary
Convert that 32-bit binary into integers, and save to array for the FFT
"""
for i in range(self.buffersToRecord):
try:
audioString = self.inStream.read(self.buffersize)
except IOError:
print "Overflow error from 'audiostring = inStream.read(buffersize)'. Try decreasing buffersize."
quit()
self.audio[i*self.buffersize:(i + 1)*self.buffersize] = numpy.fromstring(audioString,dtype = "uint32")
"""
Get int array of strength for each bin of frequencies via fast fourier transform
Get the indices of the strongest frequencies (the top 'numInput')
Scale the indices so that the frequencies fit to within numCols
Pick out the unique indices (we've reduced the mapping, so we likely have multiples)
Encode those indices into an SDR via the SparsePassThroughEncoder
Cast the SDR as a float for the TP
"""
ys = self.fft(self.audio, self.highpass, self.lowpass)
fs = numpy.sort(ys.argsort()[-self.numInput:])
rfs = fs.astype(numpy.float32) / (self.lowpass - self.highpass) * self.numCols
ufs = numpy.unique(rfs)
actualInt = self.e.encode(ufs)
actual = actualInt.astype(numpy.float32)
"""
Pass the SDR to the TP
Collect the prediction SDR from the TP
Pass the prediction & actual SDRS to the anomaly calculator & array comparer
Update the frequency plot
"""
self.tp.compute(actual, enableLearn = True, computeInfOutput = True)
predictedInt = self.tp.getPredictedState().max(axis=1)
compare = self.vis.compareArray(actualInt, predictedInt)
anomaly = self.vis.calcAnomaly(actualInt, predictedInt)
print "." . join(compare)
print self.vis.hashtagAnomaly(anomaly)
self.freqPlot.set_ydata(ys)
plt.show(block = False)
plt.draw()
def fft(self, audio, highpass, lowpass):
"""
Fast fourier transform conditioning
Output:
'output' contains the strength of each frequency in the audio signal
frequencies are marked by its position in 'output':
frequency = index * rate / buffesize
output.size = buffersize/2
Method:
Use numpy's FFT (numpy.fft.fft)
Find the magnitude of the complex numbers returned (abs value)
Split the FFT array in half, because we have mirror frequencies
(they're the complex conjugates)
Use just the first half to apply the bandpass filter
Great info here: http://stackoverflow.com/questions/4364823/how-to-get-frequency-from-fft-result
"""
left,right = numpy.split(numpy.abs(numpy.fft.fft(audio)),2)
output = left[highpass:lowpass]
return output
# train temporal pooler with desired state
#desiredState = exputils.getRandom2dBoolMatrix(inputWidth, inputHeight).flatten()
#print "training temporal pooler"
#for x in xrange(0, inputWidth * inputHeight * 100):
# temporalPooler.compute(bottomUpInput = desiredState, enableLearn = True, computeInfOutput = False)
#print "training temporal pooler complete\n"
#temporalPooler.printCells()
# train temporal pooler with random data
print "training temporal pooler"
for x in xrange(0, inputWidth * inputHeight * 100):
trainingData = exputils.getRandom2dBoolMatrix(inputWidth,
inputHeight).flatten()
temporalPooler.compute(bottomUpInput=trainingData,
enableLearn=True,
computeInfOutput=False)
print "training temporal pooler complete\n"
#temporalPooler.printCells()
#--------------------- define transform -----------------------
def applyTransform(base, manipulator):
# simple bit flip
for x in range(0, len(manipulator)):
if manipulator[x] == 1:
baseValue = base[x]
if baseValue == 0:
baseValue = 1
else:
tp = TP(numberOfCols=date_enc.width, cellsPerColumn=1795,
initialPerm=0.5, connectedPerm=0.5,
minThreshold=10, newSynapseCount=10,
permanenceInc=0.1, permanenceDec=0.01,
activationThreshold=5,
globalDecay=0, burnIn=1,
checkSynapseConsistency=False,
pamLength=7)
days = [datetime.date(y, m, d) for y in range(1998, 2013) for m in range(1, 13) for d in range(1, calendar.monthrange(y, m)[1] + 1)]
print days[0], days[1], days[-2], days[-1]
input_array = numpy.zeros(date_enc.width, dtype="int32")
for pres in xrange(10):
print 'Pass', pres
for i, d in enumerate(days):
if (i + 1) % 100 == 0:
print i + 1
if (i + 1) % 28 == 0:
tp.reset()
input_array[:] = numpy.concatenate(date_enc.encodeEachField(DateRecord(d)))
tp.compute(input_array, enableLearn=True, computeInfOutput=False)
#input_array[:] = numpy.concatenate(date_enc.encodeEachField(DateRecord(days[i + 1])))
#tp.compute(input_array, enableLearn=True, computeInfOutput=False)
print "saving TP to tp.p and tp.tp"
tp.saveToFile("tp.tp")
with open("tp.p", "w") as f:
pickle.dump(tp, f)
globalDecay=0,
burnIn=1,
checkSynapseConsistency=False,
pamLength=7)
days = [
datetime.date(y, m, d) for y in range(1998, 2013)
for m in range(1, 13) for d in range(1,
calendar.monthrange(y, m)[1] + 1)
]
print days[0], days[1], days[-2], days[-1]
input_array = numpy.zeros(date_enc.width, dtype="int32")
for pres in xrange(10):
print 'Pass', pres
for i, d in enumerate(days):
if (i + 1) % 100 == 0:
print i + 1
if (i + 1) % 28 == 0:
tp.reset()
input_array[:] = numpy.concatenate(
date_enc.encodeEachField(DateRecord(d)))
tp.compute(input_array, enableLearn=True, computeInfOutput=False)
#input_array[:] = numpy.concatenate(date_enc.encodeEachField(DateRecord(days[i + 1])))
#tp.compute(input_array, enableLearn=True, computeInfOutput=False)
print "saving TP to tp.p and tp.tp"
tp.saveToFile("tp.tp")
with open("tp.p", "w") as f:
pickle.dump(tp, f)
def main():
# create Temporal Pooler instance
tp = TP(numberOfCols=50, # カラム数
cellsPerColumn=2, # 1カラム中のセル数
initialPerm=0.5, # initial permanence
connectedPerm=0.5, # permanence の閾値
minThreshold=10, # 末梢樹状セグメントの閾値の下限?
newSynapseCount=10, # ?
permanenceInc=0.1, # permanenceの増加
permanenceDec=0.0, # permanenceの減少
activationThreshold=8, # synapseの発火がこれ以上かを確認している.
globalDecay=0, # decrease permanence?
burnIn=1, # Used for evaluating the prediction score
checkSynapseConsistency=False,
pamLength=10 # Number of time steps
)
# 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 # A
x[1,10:20] = 1 # B
x[2,20:30] = 1 # C
x[3,30:40] = 1 # D
x[4,40:50] = 1 # E
print x
# repeat the sequence 10 times
for i in range(10):
# Send each letter in the sequence in order
# A -> B -> C -> D -> E
print
print
print '#### :', i
for j in range(5):
tp.compute(x[j], enableLearn = True, computeInfOutput=True)
#tp.printCells(predictedOnly=False)
tp.printStates(printPrevious = False, printLearnState = False)
# sequenceの最後を教える. 絶対必要なわけではないが, あった方が学習速い.
tp.reset()
for j in range(5):
print "\n\n--------","ABCDE"[j],"-----------"
print "Raw input vector\n",formatRow(x[j])
# Send each vector to the TP, with learning turned off
tp.compute(x[j], enableLearn = False, computeInfOutput = True)
# print predict state
print "\nAll the active and predicted cells:"
tp.printStates(printPrevious = False, printLearnState = False)
# get predict state
print "\n\nThe following columns are predicted by the temporal pooler. This"
print "should correspond to columns in the *next* item in the sequence."
predictedCells = tp.getPredictedState()
print formatRow(predictedCells.max(axis=1).nonzero())
