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@ -34,11 +34,11 @@ def initHook(settings, file_list, **kwargs):
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settings.pool_size = sys.maxint
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#Use a time seires of the past as feature.
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#Dense_vector's expression form is [float,float,...,float]
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settings.slots = [dense_vector(TERM_NUM)]
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settings.input_types = [dense_vector(TERM_NUM)]
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#There are next FORECASTING_NUM fragments you need predict.
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#Every predicted condition at time point has four states.
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for i in range(FORECASTING_NUM):
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settings.slots.append(integer_value(LABEL_VALUE_NUM))
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settings.input_types.append(integer_value(LABEL_VALUE_NUM))
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@provider(
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@ -57,7 +57,7 @@ def process(settings, file_name):
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pre_spd = map(float, speeds[i - TERM_NUM:i])
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# Integer value need predicting, values start from 0, so every one minus 1.
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fol_spd = [i - 1 for i in speeds[i:i + FORECASTING_NUM]]
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fol_spd = [j - 1 for j in speeds[i:i + FORECASTING_NUM]]
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# Predicting label is missing, abandon the sample.
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if -1 in fol_spd:
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@ -67,7 +67,7 @@ def process(settings, file_name):
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def predict_initHook(settings, file_list, **kwargs):
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settings.pool_size = sys.maxint
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settings.slots = [dense_vector(TERM_NUM)]
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settings.input_types = [dense_vector(TERM_NUM)]
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@provider(init_hook=predict_initHook, should_shuffle=False)
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