def dataWrapper():
dataSet = splitData(df = omitOutliers())
trainSet = dataSet['trainSet']
testSet = dataSet['testSet']
trainSetX = trainSet.loc[:,'mcg':'nuc']
trainSetClass = trainSet['Class']
testSetX = testSet.loc[:,'mcg':'nuc']
testSetClass = testSet['Class']
return {'trainX':trainSetX, 'trainClass':trainSetClass,'testX':testSetX,'testClass':testSetClass}
def setData():
data = loadData()
dataSet = splitData(data)
trainSet = dataSet['trainSet']
testSet = dataSet['testSet']
xtrain = np.array(trainSet.loc[:,'mcg':'nuc'])
ytrain = np.array(trainSet['Class'])
ytrain = np.array(pd.get_dummies(ytrain))
#print(ytrain)
#print(len(ytrain[0]))
xtest = np.array(testSet.loc[:,'mcg':'nuc'])
ytest = np.array(testSet['Class'])
ytest = np.array(pd.get_dummies(ytest))
activation_1 = layers.Dense(units=3, activation='sigmoid') #First layer
activation_2 = layers.Dense(units=3, activation='sigmoid') #Second layer
output_layer = layers.Dense(units=10,activation='softmax') #Output layer
#To build up the model
model = Sequential([activation_1,activation_2,output_layer]) #initialized the model
sgd = optimizers.SGD(lr=0.1)
model.compile(loss='categorical_crossentropy',optimizer=sgd,metrics=['accuracy'])
weight_receive= []
print_weights = LambdaCallback(on_epoch_end=lambda batch, logs: ([weight_receive.append(output_layer.get_weights())]).append(activation_2.get_weights()))
history = model.fit(xtrain, ytrain, epochs=50,batch_size=1,verbose=0,callbacks = [print_weights])
error = []
for i in range(len(history.history['acc'])):
error.append(1-(history.history['acc'][i]))
output_weight = output_layer.get_weights()
layer2 = activation_2.get_weights()
print('Training error:',error[len(error)-1])
print('Outputlayer all weights:','\n',output_weight[0])
print('Outputlayer bias:','\n',output_weight[1])
print('Second Hidden layer all weights:','\n',layer2[0])
print('Second Hidden layer bias:','\n',layer2[1])
plt.xlim(0,4)
plt.ylim(0,10)
outtemp = avrage_data(data,run_or_walk)
xa, moving_avg, min_sigma, max_sigma = outtemp['frequency'], outtemp['avg'], outtemp['min sigma'], outtemp['max sigma']
plt.fill_between(xa['frequency'],min_sigma,max_sigma,alpha=0.4)
plt.plot(xa['frequency'],moving_avg,'r')
#plt.plot(x['frequency'], x['magnitude'])
def median(lst): return np.median(np.array(lst))
def mean(lst): return sum(lst)/len(lst)
if __name__ == '__main__':
data = splitData()
plt.rc('text', usetex=True)
plt.rc('font', family='serif')
fig = plt.figure(figsize=(8,13), tight_layout=True)
# ax = list()
# ax.append()
afrequesy_plot(data,'walk',(4,1,1))
afrequesy_plot(data,'run',(4,1,2))
movingmedian_plot(data,'walk',(4,1,3))
movingmedian_plot(data,'run',(4,1,4))
plt.subplots_adjust(hspace=0.53)
plt.savefig('plot.png')
plt.show()