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walking_3_sec = data[(data['activity'] == 'walking') & (data['exp_id'] == 1)][1000:1150]
model.predict(walking_3_sec, output_frequency='per_window')

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# Save the model for later use in Turi Create
model.save('mymodel.model')

# Export for use in Core ML
model.export_coreml('MyActivityClassifier.mlmodel')

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# Evaluate the model and save the results into a dictionary
metrics = model.evaluate(test)
print(metrics['accuracy'])

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# Create an activity classifier
model = tc.activity_classifier.create(train, session_id='exp_id', target='activity', prediction_window=50)

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# Train/test split by recording sessions
train, test = tc.activity_classifier.util.random_split_by_session(data, session_id='exp_id', fraction=0.8)

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target_map = {
    1.: 'walking',          
    2.: 'climbing_upstairs',
    3.: 'climbing_downstairs',
    4.: 'sitting',
    5.: 'standing',
    6.: 'laying'
}

# Use the same labels used in the experiment

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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:])

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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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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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#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