## Python Homework Help to Predict the Syndrome Existence

• 17th Jun, 2022
• 16:00 PM
```from keras.layers import Dense, Dropout, LSTM, Embedding
from keras.models import Sequential
import pandas as pd
import numpy as np

min_len = -1
max_len = -1
X_train = []
Y_train = []
X_test = []
Y_test = []

for i in range(1, 7):
loc = 'without syndrome/control' + str(i) + '.xlsx'
data = excel_data['HR'].tolist()
if min_len == -1 or min_len > len(data):
min_len = len(data)
if max_len == -1 or max_len < len(data):
max_len = len(data)
X_train.append(data)
Y_train.append(0)

for i in range(1, 7):
loc = 'with syndrome/NAS' + str(i) + '.xlsx'
data = excel_data['HR'].tolist()
if min_len == -1 or min_len > len(data):
min_len = len(data)
if max_len == -1 or max_len < len(data):
max_len = len(data)
X_train.append(np.array(data))
Y_train.append(1)

for i in range(1, 3):
loc = 'test/test' + str(i) + '.xlsx'
data = excel_data['HR'].tolist()
if min_len == -1 or min_len > len(data):
min_len = len(data)
if max_len == -1 or max_len < len(data):
max_len = len(data)
X_test.append(data)
Y_test.append(i)

truncated = []
for xt in X_train:
truncated.append(xt[:min_len]);

truncated_test = []
for xt in X_test:
truncated_test.append(xt[:min_len]);

return min_len, np.array(truncated), np.array(Y_train), np.array(truncated_test), np.array(Y_test)

def create_model(input_length):
print('Creating model...')
model = Sequential()

print('Compiling...')
model.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=['accuracy'])
return model

if __name__ == '__main__':
min_len, X_train, Y_train, X_test, Y_test = read_data()

print(min_len)

model = create_model(min_len)

print('Fitting model...')

hist = model.fit(X_train, Y_train, batch_size=64, epochs=10, validation_split=0.1, verbose=1)

score, acc = model.evaluate(X_test, Y_test, batch_size=1)

print('Test score:', score)
print('Test accuracy:', acc)
```