output

C:\Users\Jonathan\Documents\GitHub\htm-over-http\test [master +1 ~0 -0 !]> python .\hotgym.py

Feed the data from rec-center-hourly.csv into the
htm REST API and compare the results with the
standard results provided in the tutorial

Made model 25b22f16-196a-4fcf-af5a-1c5d9939df03
{'timestamp': 1277994600, 'kw_energy_consumption': '21.2'}
{u'likelihood': 0.5, u'anomaly_score': 1.0, u'prediction': 21.2}
{'timestamp': 1277998200, 'kw_energy_consumption': '16.4'}
{u'likelihood': 0.5, u'anomaly_score': 1.0, u'prediction': 16.4}
{'timestamp': 1278001800, 'kw_energy_consumption': '4.7'}
{u'likelihood': 0.5, u'anomaly_score': 1.0, u'prediction': 16.4}
{'timestamp': 1278005400, 'kw_energy_consumption': '4.7'}
{u'likelihood': 0.5, u'anomaly_score': 0.0, u'prediction': 4.7}
{'timestamp': 1278009000, 'kw_energy_consumption': '4.6'}
{u'likelihood': 0.5, u'anomaly_score': 0.0, u'prediction': 4.7}
{'timestamp': 1278012600, 'kw_energy_consumption': '23.5'}
{u'likelihood': 0.5, u'anomaly_score': 0.0, u'prediction': 4.67}
{'timestamp': 1278016200, 'kw_energy_consumption': '47.5'}
{u'likelihood': 0.5, u'anomaly_score': 0.0, u'prediction': 4.67}
{'timestamp': 1278019800, 'kw_energy_consumption': '45.4'}
{u'likelihood': 0.5, u'anomaly_score': 0.0, u'prediction': 4.67}
{'timestamp': 1278023400, 'kw_energy_consumption': '46.1'}
{u'likelihood': 0.5, u'anomaly_score': 0.0, u'prediction': 4.67}
{'timestamp': 1278027000, 'kw_energy_consumption': '41.5'}
{u'likelihood': 0.5, u'anomaly_score': 0.0, u'prediction': 4.67}
{'timestamp': 1278030600, 'kw_energy_consumption': '43.4'}
{u'likelihood': 0.5, u'anomaly_score': 0.0, u'prediction': 4.67}
{'timestamp': 1278034200, 'kw_energy_consumption': '43.8'}
{u'likelihood': 0.5, u'anomaly_score': 0.0, u'prediction': 4.67}
{'timestamp': 1278037800, 'kw_energy_consumption': '37.8'}
{u'likelihood': 0.5, u'anomaly_score': 0.0, u'prediction': 4.67}
{'timestamp': 1278041400, 'kw_energy_consumption': '36.6'}
{u'likelihood': 0.5, u'anomaly_score': 0.0, u'prediction': 4.67}
{'timestamp': 1278045000, 'kw_energy_consumption': '35.7'}
{u'likelihood': 0.5, u'anomaly_score': 0.0, u'prediction': 4.67}
{'timestamp': 1278048600, 'kw_energy_consumption': '38.9'}
{u'likelihood': 0.5, u'anomaly_score': 0.0, u'prediction': 4.67}
{'timestamp': 1278052200, 'kw_energy_consumption': '36.2'}
{u'likelihood': 0.5, u'anomaly_score': 0.0, u'prediction': 4.67}
{'timestamp': 1278055800, 'kw_energy_consumption': '36.6'}
{u'likelihood': 0.5, u'anomaly_score': 0.0, u'prediction': 4.67}
{'timestamp': 1278059400, 'kw_energy_consumption': '37.2'}
{u'likelihood': 0.5, u'anomaly_score': 0.0, u'prediction': 4.67}
{'timestamp': 1278063000, 'kw_energy_consumption': '38.2'}
{u'likelihood': 0.5, u'anomaly_score': 0.0, u'prediction': 4.67}
{'timestamp': 1278066600, 'kw_energy_consumption': '14.1'}
{u'likelihood': 0.5, u'anomaly_score': 0.0, u'prediction': 4.67}
{'timestamp': 1278070200, 'kw_energy_consumption': '5.1'}
{u'likelihood': 0.5, u'anomaly_score': 0.0, u'prediction': 4.67}
{'timestamp': 1278073800, 'kw_energy_consumption': '5'}