def createDiagnosticsLayout(signal, frequency, holidayDropdown, holidayScale, seasonalityScale, changepointScale, seasonalityMode, contents, filename, paramSearch):
print(signal, frequency, holidayDropdown, holidayScale, seasonalityScale, changepointScale, seasonalityMode, filename, paramSearch)
if signal == 'VOID':
return None
if signal != 'NOTIFY':
return html.Div('Encountered an error : ' + signal, style = {'margin-left' : '1rem'})
df = parseContents(contents, filename)
model = generateModel(frequency, holidayDropdown, holidayScale, seasonalityScale, changepointScale, seasonalityMode, df, paramSearch)
initial, period, horizon = getParams(frequency, len(df))
df_cv =cross_validation(model, initial = initial, period = period, horizon = horizon)
#df_p = performance_metrics(df_cv, rolling_window = 0)
#print(df_p.head())
fig = mpl_to_plotly(plot_cross_validation_metric(df_cv, metric = 'mae', rolling_window = 0))
return html.Div(children = [html.H6('Mean Absolute Error', style = {'margin-left': '1rem'}), dcc.Graph(figure = fig)])
def createComponentLayout(signal, frequency, holidayDropdown, holidayScale,
seasonalityScale, changepointScale, seasonalityMode,
contents, filename, paramSearch):
print(signal, frequency, holidayDropdown, holidayScale, seasonalityScale,
changepointScale, seasonalityMode, filename, paramSearch)
if signal == 'VOID':
return None
if signal != 'NOTIFY':
return html.Div('Encountered an error : ' + signal,
style={'margin-left': '1rem'})
df = parseContents(contents, filename)
model = generateModel(frequency, None, holidayScale, seasonalityScale,
changepointScale, seasonalityMode, df, paramSearch)
future = model.make_future_dataframe(periods=int(0.1 * len(df)),
freq=frequency)
forecast = model.predict(future)
fig = plot_components_plotly(model, forecast)
return dcc.Graph(figure=fig)
print("[+] Creating gatherData.php")
createGatherData(absolute_output_path, [key for key in entities_dict])
print("[+] Creating Database.php class")
createDatabase(absolute_output_path)
for key in entities_dict:
entityName = key
attributesList = []
actionList = []
for attribute in entities_dict[key]['attributes']:
attributesList.append(attribute)
for action in entities_dict[key]['actions']:
actionList.append(action)
print("[+] Creating Controller for %s" % entityName)
generateController(entityName, actionList, absolute_output_path)
print("[+] Creating Model for %s" % entityName)
generateModel(entityName, attributesList, absolute_output_path)
print("[+] Appending to gatherData.php for %s" % entityName)
appendToGatherData(entityName, attributesList, absolute_output_path)
closeGatherData(absolute_output_path)
print("[+] Creating index.php")
if arguments.index:
createIndex(absolute_output_path)
print("[+] Finished successfully.")
if __name__ == '__main__':
main()
def main():
import os
import sys
import numpy as np
if (len(sys.argv) == 1):
print "a0 = sys.argv[1], b0 = sys.argv[2]. alpha = a0*exp(-i*b0). "
a0 = 10000
b0 = 0.05
print "The default value of a0 =", a0, " and b0 = ", b0, " has been chosen. "
if (len(sys.argv) == 2):
a0 = float(sys.argv[1])
b0 = 0.05
if (len(sys.argv) == 3):
a0 = float(sys.argv[1])
b0 = float(sys.argv[2])
Greal = "G_real_rotated.txt"
Gimag = "G_imag_rotated.txt"
omega_n, G_real_rotated, G_imag_rotated = readFiles(Greal, Gimag)
Niom = len(omega_n)
if (not os.path.exists("model.txt")):
mu = 0
sigma = 1
model.generateModel(mu, sigma)
if (not os.path.exists("A_initial.txt")):
os.system("cp model.txt A_initial.txt")
omega, A_initial = read_spectral("A_initial.txt")
Nomega = len(omega)
D = np.zeros(Nomega)
for i in range(Nomega):
D[i] = A_initial[i]
K_real_rotated = np.zeros((Niom, Nomega))
K_imag_rotated = np.zeros((Niom, Nomega))
ifile = open("K_real_rotated.txt", "r")
for i in range(Niom):
for j in range(Nomega):
string = ifile.readline()
a = string.split()
K_real_rotated[i, j] = float(a[2])
ifile.close()
ifile = open("K_imag_rotated.txt", "r")
for i in range(Niom):
for j in range(Nomega):
string = ifile.readline()
a = string.split()
K_imag_rotated[i, j] = float(a[2])
ifile.close()
singular_values = []
ifile = open("s.txt", "r")
for index, string in enumerate(ifile):
singular_values.append(float(string))
ifile.close()
if (len(singular_values) != Niom):
sys.exit(
"Number of singular values should be equal to the number of Matsubara frequencies. "
)
LambdaInverse = np.zeros((Niom, Niom))
for i in range(Niom):
LambdaInverse[i, i] = 1.0 / singular_values[i]
if (True):
alpha = []
error = []
for i in range(30):
alpha.append(a0 * np.exp(-i * b0))
ofile = open("alpha.txt", "a")
for i in range(len(alpha)):
#A_updated = newton.newton(alpha[i], G_real_rotated, G_imag_rotated, K_real_rotated, K_imag_rotated, omega, A_initial, D, LambdaInverse)
A_updated = solver.solver(alpha[i], G_real_rotated, G_imag_rotated,
K_real_rotated, K_imag_rotated, omega,
A_initial, D, LambdaInverse)
if (nan.array_isnan(A_updated)):
omega, A_initial = read_spectral("A_initial.txt")
continue
output = "A_updated_alpha_" + str(alpha[i]) + ".txt"
printFile.printFile(omega, A_updated, output)
os.system("cp " + output + " A_initial.txt")
ofile.write(str(alpha[i]) + "\n")
diff = norm(A_updated - A_initial)
error.append(diff)
print "alpha = ", alpha[i], ", error = ", diff
A_initial = A_updated
ofile.close()
printFile.printFile(alpha, error, "error_alpha.txt")
os.system("python pltfiles.py A_initial.txt")
return 0
)
sys.exit()
if (saveMFCC == 1):
print("Saving training MFCCs...")
# Generation of training MFCCs
for g in gameNames:
generateTrainingMFCCs('audio/train', 'labels', g)
# If user wants to train the model
if (trainingBool == 1):
print("Training model on train MFCCs...")
nEpochs = 10
# load mfcc correctly
model = generateModel()
tf.keras.utils.plot_model(model, to_file='model.png', show_shapes=True)
model.compile(loss='binary_crossentropy',
optimizer=keras.optimizers.Adam(lr=0.0001),
metrics=['accuracy'])
trainModel(nEpochs, gameNames, model)
if (trainingBool == 0):
print("Loading pre-existing model...")
model = generateModel()
model.load_weights('model_weights.h5')
if (SHOW_ARCH == 1):
def main():
import sys
import os
import numpy as np
omega_n = []
ifile = open("g_imag.txt", "r")
for index, string in enumerate(ifile):
omega_n.append(float(string.split()[0]))
ifile.close()
Niom = len(omega_n)
G_real = np.zeros(Niom)
G_imag = np.zeros(Niom)
ifile = open("g_real.txt", "r")
for index, string in enumerate(ifile):
G_real[index] = float(string.split()[1])
ifile.close()
ifile = open("g_imag.txt", "r")
for index, string in enumerate(ifile):
G_imag[index] = float(string.split()[1])
ifile.close()
C_real = np.zeros((Niom, Niom))
ifile = open("C_real.txt")
for i in range(Niom):
for j in range(Niom):
string = ifile.readline()
C_real[i, j] = float(string.split()[2])
ifile.close()
u, s, ut = np.linalg.svd(C_real) #C_real = u.dot(s).dot(ut)
ofile = open("eigenvalues.txt", "w")
for i in range(len(s)):
ofile.write(str(s[i]) + "\n")
ofile.close()
ofile = open("eig_log.txt", "w")
for i in range(len(s)):
ofile.write(str(i + 1) + " " + str(np.log(s[i])) + "\n")
ofile.close()
ratio = 1.0e-7
counter = 0
for i in range(Niom):
if (s[i] >= ratio * s[0]):
counter = counter + 1
ofile = open("s.txt", "w")
for i in range(counter):
ofile.write(str(s[i]) + "\n")
ofile.close()
u_truncated = np.zeros((counter, counter))
for i in range(counter):
for j in range(counter):
u_truncated[i, j] = u[i, j]
G_real_truncated = np.zeros(counter)
G_imag_truncated = np.zeros(counter)
for i in range(counter):
G_real_truncated[i] = G_real[i]
G_imag_truncated[i] = G_imag[i]
G_real_rotated = np.transpose(u_truncated).dot(G_real_truncated)
G_imag_rotated = np.transpose(u_truncated).dot(G_imag_truncated)
ofile = open("G_real_rotated.txt", "w")
for i in range(len(G_real_rotated)):
ofile.write(str(omega_n[i]) + " " + str(G_real_rotated[i]) + "\n")
ofile.close()
ofile = open("G_imag_rotated.txt", "w")
for i in range(len(G_imag_rotated)):
ofile.write(str(omega_n[i]) + " " + str(G_imag_rotated[i]) + "\n")
ofile.close()
if (not os.path.exists("model.txt")):
model.generateModel(0, 1)
omega = []
model_function = []
ifile = open("model.txt", "r")
for i, string in enumerate(ifile):
a = string.split()
omega.append(float(a[0]))
model_function.append(float(a[1]))
ifile.close()
Nomega = len(omega)
K_matrix_real = kernel.K_matrix_real(omega_n, omega)
K_matrix_imag = kernel.K_matrix_imag(omega_n, omega)
K_real_truncated = np.zeros((counter, Nomega))
K_imag_truncated = np.zeros((counter, Nomega))
for i in range(counter):
for j in range(Nomega):
K_real_truncated[i, j] = K_matrix_real[i, j]
K_imag_truncated[i, j] = K_matrix_imag[i, j]
K_real_rotated = np.transpose(u_truncated).dot(K_real_truncated)
K_imag_rotated = np.transpose(u_truncated).dot(K_imag_truncated)
printFile.printMatrix(K_real_rotated, "K_real_rotated.txt")
printFile.printMatrix(K_imag_rotated, "K_imag_rotated.txt")
return 0