Python NormalizedSpectrograms Examples

Example #1

    # Collapse strided so that it has one more dimension than the window.  I.e.,
    # the new array is a flat list of slices.
    meat = len(ws) if ws.shape else 0
    firstdim = (np.product(newshape[:-meat]), ) if ws.shape else ()
    dim = firstdim + (newshape[-meat:])
    return strided.reshape(dim)


#(X_train, y_train), (X_test, y_test) = full_bpm_to_data(get_interesting_heartrates(HEART_AV_ROOT))

ns = NormalizedSubjectSplitSpectrograms(
    subjectIdependant=True)  #NormalizedSpectrograms()

#ns = NormalizedSubjectSplitSpectrograms(subjectIdependant=False)#NormalizedSpectrograms()
ns = NormalizedSpectrograms(getVideoSpectrograms())

X_train, Y_train = ns.getTrainData()
print(X_train.shape)
ws = np.array(X_train.shape)
ss = np.array(X_train.shape)
ws = (1, 1, 200, 50)
ss = (1, 1, 200, 5)
X_train = sliding_window(X_train, ws, ss, True)
X_train = X_train[:, 0, :, :, :]
X_val, Y_val = ns.getValidationData()
X_val = X_val[:, :, :, 0:50]

repeat_cnt = 4
Y_train = repeat_n_times(Y_train, repeat_cnt)
Y_train = np.repeat(Y_train, X_train.shape[0] // Y_train.shape[0], axis=0)

Example #2

File: rnnAproach.py Project: stormmind/Palpitate

from spectrogram import full_bpm_to_data, HEART_AV_ROOT, NormalizedSpectrograms, NormalizedSubjectSplitSpectrograms, getVideoSpectrograms
from get_heartrates import get_interesting_heartrates
from keras.callbacks import EarlyStopping
from kbhit import KBHit

import numpy as np
import code
import random
import learnLib

kb = KBHit()
#(X_train, y_train), (X_test, y_test) = full_bpm_to_data(get_interesting_heartrates(HEART_AV_ROOT))

#ns = NormalizedSubjectSplitSpectrograms(subjectIdependant=False)#NormalizedSpectrograms()
ns = NormalizedSpectrograms(getVideoSpectrograms())


def sliceToTimeSeries(X):
    divisibleTime = X[:, 0, :, :21]
    slicedTime = np.reshape(divisibleTime, (-1, X.shape[2], 7, 3))
    swappedAxes = np.swapaxes(slicedTime, 1, 2)
    flattenLastTwo = np.reshape(swappedAxes, (X.shape[0], 7, -1))
    return flattenLastTwo


X_train, Y_train = ns.getTrainData()
X_val, Y_val = ns.getValidationData()

#slice the spectrogram
X_train = sliceToTimeSeries(X_train)
print(X_train.shape)

Example #3

File: mlpAproach.py Project: kren1/Palpitate

from spectrogram import full_bpm_to_data, HEART_AV_ROOT, NormalizedSpectrograms
from get_heartrates import get_interesting_heartrates
from keras.callbacks import EarlyStopping
from kbhit import KBHit

import numpy as np
import code
import random
import learnLib

kb = KBHit()
#(X_train, y_train), (X_test, y_test) = full_bpm_to_data(get_interesting_heartrates(HEART_AV_ROOT))

ns = NormalizedSpectrograms()

(X_train, Y_train) , valTuple = ns.getTrainAndValidationData()

print(X_train.shape)


print("Model: nb_hiddens, drop1s")


prevLoss =  34534645735673
maxModel = None
stop = False
models = {}
X_validate, Y_validate = valTuple
for args in learnLib.RandomMlpParameters(): #itertools.product(nb_hiddens, drop1s):
    print("Model: ", args)
    model = learnLib.get_2_layer_MLP_model(X_train[0].shape, *args)

Example #4

File: rnnAproach.py Project: Kevinwenya/Palpitate

from spectrogram import full_bpm_to_data, HEART_AV_ROOT, NormalizedSpectrograms, NormalizedSubjectSplitSpectrograms, getVideoSpectrograms
from get_heartrates import get_interesting_heartrates
from keras.callbacks import EarlyStopping
from kbhit import KBHit

import numpy as np
import code
import random
import learnLib

kb = KBHit()
#(X_train, y_train), (X_test, y_test) = full_bpm_to_data(get_interesting_heartrates(HEART_AV_ROOT))

#ns = NormalizedSubjectSplitSpectrograms(subjectIdependant=False)#NormalizedSpectrograms()
ns = NormalizedSpectrograms(getVideoSpectrograms())

def sliceToTimeSeries(X):
    divisibleTime = X[:,0,:,:21]
    slicedTime = np.reshape(divisibleTime, (-1, X.shape[2], 7, 3))
    swappedAxes = np.swapaxes(slicedTime, 1, 2)
    flattenLastTwo = np.reshape(swappedAxes,(X.shape[0],7 , -1))
    return flattenLastTwo


X_train, Y_train  = ns.getTrainData()
X_val, Y_val = ns.getValidationData()

#slice the spectrogram
X_train = sliceToTimeSeries(X_train)
print(X_train.shape)
#Y_train = np.repeat(np.reshape(-1,1), X_train.shape[1], axis=1)

Example #5

File: rnnAproach.py Project: kren1/Palpitate

from spectrogram import full_bpm_to_data, HEART_AV_ROOT, NormalizedSpectrograms
from get_heartrates import get_interesting_heartrates
from keras.callbacks import EarlyStopping
from kbhit import KBHit

import numpy as np
import code
import random
import learnLib

kb = KBHit()
# (X_train, y_train), (X_test, y_test) = full_bpm_to_data(get_interesting_heartrates(HEART_AV_ROOT))

ns = NormalizedSpectrograms()


def sliceToTimeSeries(X):
    divisibleTime = X[:, 0, :, :150]
    slicedTime = np.reshape(divisibleTime, (-1, X.shape[2], 30, 5))
    swappedAxes = np.swapaxes(slicedTime, 1, 2)
    flattenLastTwo = np.reshape(swappedAxes, (X.shape[0], 30, -1))
    return flattenLastTwo


(X_train, Y_train), (X_val, Y_val) = ns.getTrainAndValidationData()

# slice the spectrogram
X_train = sliceToTimeSeries(X_train)
print(X_train.shape)
# Y_train = np.repeat(np.reshape(-1,1), X_train.shape[1], axis=1)
print(Y_train.shape)

Example #6

File: mlpAproach.py Project: stormmind/Palpitate

from spectrogram import full_bpm_to_data, HEART_AV_ROOT, NormalizedSpectrograms, NormalizedSubjectSplitSpectrograms, getVideoSpectrograms
from get_heartrates import get_interesting_heartrates
from keras.callbacks import EarlyStopping
from kbhit import KBHit

import numpy as np
import code
import random
import learnLib

kb = KBHit()
#(X_train, y_train), (X_test, y_test) = full_bpm_to_data(get_interesting_heartrates(HEART_AV_ROOT))

#ns = NormalizedSubjectSplitSpectrograms()
ns = NormalizedSpectrograms(getVideoSpectrograms())

(X_train, Y_train) = ns.getTrainData()
valTuple = ns.getValidationData()

print(X_train.shape)


print("Model: nb_hiddens, drop1s")


prevLoss =  34534645735673
maxModel = None
stop = False
models = {}
X_validate, Y_validate = valTuple
for args in learnLib.RandomMlpParameters(): #itertools.product(nb_hiddens, drop1s):