def calculate_values_to_forecast_exception_test(self):
"""Test for correct handling of illegal TimeSeries instances.
@todo remove NotImplementedError Catch."""
data = [[1.5, 152.0], [2.5, 172.8], [3.5, 195.07200000000003],
[4.5, 218.30528000000004]]
ts = TimeSeries.from_twodim_list(data)
ts.add_entry(3, 1343)
bfm = BaseForecastingMethod()
# nothing has to be done, because forecast_until was never called
bfm._calculate_values_to_forecast(ts)
bfm.forecast_until(134)
try:
bfm._calculate_values_to_forecast(ts)
except ValueError:
pass
else:
assert False # pragma: no cover
ts.sort_timeseries()
try:
bfm._calculate_values_to_forecast(ts)
except ValueError:
pass
else:
assert False # pragma: no cover
ts.normalize("second")
bfm._calculate_values_to_forecast(ts)
def calculate_values_to_forecast_exception_test(self):
"""Test for correct handling of illegal TimeSeries instances.
@todo remove NotImplementedError Catch."""
data = [[1.5, 152.0],[2.5, 172.8],[3.5, 195.07200000000003],[4.5, 218.30528000000004]]
ts = TimeSeries.from_twodim_list(data)
ts.add_entry(3, 1343)
bfm = BaseForecastingMethod()
# nothing has to be done, because forecast_until was never called
bfm._calculate_values_to_forecast(ts)
bfm.forecast_until(134)
try:
bfm._calculate_values_to_forecast(ts)
except ValueError:
pass
else:
assert False # pragma: no cover
ts.sort_timeseries()
try:
bfm._calculate_values_to_forecast(ts)
except ValueError:
pass
else:
assert False # pragma: no cover
ts.normalize("second")
bfm._calculate_values_to_forecast(ts)
def number_of_values_to_forecast_test(self):
"""Test the valid calculation of values to forecast."""
data = [[1.5, 152.0],[2.5, 172.8],[3.5, 195.07200000000003],[4.5, 218.30528000000004]]
ts = TimeSeries.from_twodim_list(data)
ts.normalize("second")
bfm = BaseForecastingMethod()
bfm.forecast_until(100)
bfm._calculate_values_to_forecast(ts)
assert bfm.get_parameter("valuesToForecast") == 96
def number_of_values_to_forecast_test(self):
"""Test the valid calculation of values to forecast."""
data = [[1.5, 152.0], [2.5, 172.8], [3.5, 195.07200000000003],
[4.5, 218.30528000000004]]
ts = TimeSeries.from_twodim_list(data)
ts.normalize("second")
bfm = BaseForecastingMethod()
bfm.forecast_until(100)
bfm._calculate_values_to_forecast(ts)
assert bfm.get_parameter("valuesToForecast") == 96
