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#Encoding Categorical Data
from sklearn.preprocessing import LabelEncoder
labelencoder = LabelEncoder()

y = labelencoder.fit_transform(y)

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# Importing the Keras libraries and packages
import keras
from keras.models import Sequential
from keras.layers import Dense

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# Initialising the ANN
classifier = Sequential()

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# Adding the input layer and the first hidden layer
classifier.add(Dense(units = 16, kernel_initializer = 'uniform', activation = 'relu', input_dim = 30))

# Adding the second hidden layer
classifier.add(Dense(units = 16, kernel_initializer = 'uniform', activation = 'relu'))

# Adding the output layer
classifier.add(Dense(units = 1, kernel_initializer = 'uniform', activation = 'sigmoid'))

# Compiling the ANN

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# Fitting the ANN to the Training set
classifier.fit(X_train, y_train, batch_size = 10, epochs = 100)

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# Predicting the Test set results
y_pred = classifier.predict(X_test)
y_pred = (y_pred > 0.5)

# Making the Confusion Matrix
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred) 

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#Uploading the Dataset 
from google.colab import files
uploaded = files.upload()


with open("HAPTdataset.zip", 'w') as f:
    f.write(uploaded[uploaded.keys()[0]])

import zipfile

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import turicreate as tc

data_dir = './HAPTdataset/RawData/'

# Load labels
labels = tc.SFrame.read_csv(data_dir + 'labels.txt', delimiter=' ', header=False, verbose=False)
labels = labels.rename({'X1': 'exp_id', 'X2': 'user_id', 'X3': 'activity_id', 'X4': 'start', 'X5': 'end'})
labels

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def find_label_for_containing_interval(intervals, index):
    containing_interval = intervals[:, 0][(intervals[:, 1] <= index) & (index <= intervals[:, 2])]
    if len(containing_interval) == 1:
        return containing_interval[0]

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from glob import glob

acc_files = glob(data_dir + 'acc_*.txt')
gyro_files = glob(data_dir + 'gyro_*.txt')

# Load data
data = tc.SFrame()
files = zip(sorted(acc_files), sorted(gyro_files))
for acc_file, gyro_file in files:
    exp_id = int(acc_file.split('_')[1][-2:])