from spectrogram import bpm_to_data
from get_heartrates import get_interesting_heartrates
from convertToWav import VIDEO_ROOT
from keras.models import Sequential
from keras.layers.core import Dense, Activation, Dropout, Flatten
from keras.layers.convolutional import Convolution2D, MaxPooling2D
from keras.optimizers import SGD
from keras.utils import np_utils, generic_utils
import random
import numpy as np
import code
#dumy_data_subject1 = [(i*5 + 1, random.randint(lowest_bpm, highest_bpm)) for i in range(1,200)]
#dumy_data = {35 : dumy_data_subject1}
(X_train, y_train), (X_test, y_test) = bpm_to_data(get_interesting_heartrates("C:\\Uni\\HeartAV"))
lowest_bpm = min(min(y_train) , min(y_test))
highest_bpm = max(max(y_train) , max(y_test))
scale = 1
nb_classes = (highest_bpm - lowest_bpm + 1*scale) // scale
#code.interact(local=locals())
print(highest_bpm, lowest_bpm, nb_classes)
#code.interact(local=locals())
#y_train = list(map(lambda x : (x - lowest_bpm)//scale, y_train))
#y_test = list(map(lambda x : (x - lowest_bpm) //scale, y_test))
# convert class vectors to binary class matrices
# converts a number to unary so 4 is 0001
#Y_train = np_utils.to_categorical(y_train, nb_classes)
import random
import numpy as np
lowest_bpm = 40
highest_bpm = 50
nb_classes = highest_bpm - lowest_bpm + 1
nb_filters = 32
nb_conv = 3
nb_pool = 2
dumy_data_subject1 = [(i*5 + 1, random.randint(lowest_bpm, highest_bpm)) for i in range(1,200)]
dumy_data = {35 : dumy_data_subject1}
(X_train, y_train), (X_test, y_test) = bpm_to_data(dumy_data)
y_train = list(map(lambda x : x - lowest_bpm, y_train))
y_test = list(map(lambda x : x - lowest_bpm, y_test))
# convert class vectors to binary class matrices
# converts a number to unary so 4 is 0001
Y_train = np_utils.to_categorical(y_train, nb_classes)
Y_test = np_utils.to_categorical(y_test, nb_classes)
print(X_train[0].shape)
model = Sequential()
model.add(Convolution2D(nb_filters, 3,3, input_shape=(X_train[0].shape)))
model.add(Activation('sigmoid'))
model.add(Convolution2D(nb_filters, 3,3))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(nb_pool, nb_pool)))
from spectrogram import bpm_to_data
from get_heartrates import get_interesting_heartrates
from convertToWav import VIDEO_ROOT
from keras.models import Sequential
from keras.layers.core import Dense, Activation, Dropout, Flatten
from keras.layers.convolutional import Convolution2D, MaxPooling2D
from keras.optimizers import SGD
from keras.utils import np_utils, generic_utils
import random
import numpy as np
import code
#dumy_data_subject1 = [(i*5 + 1, random.randint(lowest_bpm, highest_bpm)) for i in range(1,200)]
#dumy_data = {35 : dumy_data_subject1}
(X_train, y_train), (X_test, y_test) = bpm_to_data(
get_interesting_heartrates("C:\\Uni\\HeartAV"))
lowest_bpm = min(min(y_train), min(y_test))
highest_bpm = max(max(y_train), max(y_test))
scale = 1
nb_classes = (highest_bpm - lowest_bpm + 1 * scale) // scale
#code.interact(local=locals())
print(highest_bpm, lowest_bpm, nb_classes)
#code.interact(local=locals())
#y_train = list(map(lambda x : (x - lowest_bpm)//scale, y_train))
#y_test = list(map(lambda x : (x - lowest_bpm) //scale, y_test))
# convert class vectors to binary class matrices
# converts a number to unary so 4 is 0001
#Y_train = np_utils.to_categorical(y_train, nb_classes)
import random
import numpy as np
lowest_bpm = 40
highest_bpm = 50
nb_classes = highest_bpm - lowest_bpm + 1
nb_filters = 32
nb_conv = 3
nb_pool = 2
dumy_data_subject1 = [(i * 5 + 1, random.randint(lowest_bpm, highest_bpm)) for i in range(1, 200)]
dumy_data = {35: dumy_data_subject1}
(X_train, y_train), (X_test, y_test) = bpm_to_data(dumy_data)
y_train = list(map(lambda x: x - lowest_bpm, y_train))
y_test = list(map(lambda x: x - lowest_bpm, y_test))
# convert class vectors to binary class matrices
# converts a number to unary so 4 is 0001
Y_train = np_utils.to_categorical(y_train, nb_classes)
Y_test = np_utils.to_categorical(y_test, nb_classes)
print(X_train[0].shape)
model = Sequential()
model.add(Convolution2D(nb_filters, 3, 3, input_shape=(X_train[0].shape)))
model.add(Activation("sigmoid"))
model.add(Convolution2D(nb_filters, 3, 3))
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size=(nb_pool, nb_pool)))
