Python NormalizedSpectrograms.getTrainData Examples

Example #1

File: rnnAproach.py Project: Kevinwenya/Palpitate

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)
print(Y_train.shape)

print("Model: lstm outdim, nb_hiddens, drop1, drop2")


prevLoss =  34534645735673
maxModel = None
stop = False
models = {}

Example #2

File: rnnAproach.py Project: stormmind/Palpitate

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)
print(Y_train.shape)

print("Model: lstm outdim, nb_hiddens, drop1, drop2")

prevLoss = 34534645735673
maxModel = None
stop = False
models = {}
X_val = sliceToTimeSeries(X_val)

Example #3

File: mlpAproach.py Project: stormmind/Palpitate

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):
    print("Model: ", args)