def predict_with_v2(forecast, day):
    X, y, days = reader.rain_temp_snow(), reader.read_cyclist_data().ravel(), reader.day_of_week()

    clf = np.array([linear_model.LinearRegression() for i in range(7)])
    for i in range(7):
        clf[i].fit(X[days == i], y[days == i])
    prediction = clf[day].predict([forecast])
    prediction[prediction < 0] = 0
    return prediction
def predict_with_v2b(forecast, day):
    X, y, days = reader.rain_temp_snow(), reader.read_cyclist_data().ravel(), reader.day_of_week()

    clf = np.array([linear_model.LinearRegression() for i in range(2)])
    clf[0].fit(X[days < 5], y[days < 5])
    clf[1].fit(X[days > 4], y[days > 4])
    if day < 5:
        prediction = clf[0].predict([forecast])
    else:
        prediction = clf[1].predict([forecast])
    prediction[prediction < 0] = 0
    return prediction
def predict_with_v4(forecast, day):
    X, y, days = reader.rain_temp_snow_prev_week(), reader.read_cyclist_data(
    ).ravel()[7:], reader.day_of_week()[7:]

    clf = np.array([linear_model.LinearRegression() for i in range(2)])
    clf[0].fit(X[days < 5], y[days < 5])
    clf[1].fit(X[days > 4], y[days > 4])
    if day < 5:
        prediction = clf[0].predict([forecast])
    else:
        prediction = clf[1].predict([forecast])
    prediction[prediction < 0] = 0
    return prediction
def version3():
    X, y, days = shuffle(reader.rain_temp_snow_prev(),
                         reader.read_cyclist_data().ravel()[7:],
                         reader.day_of_week()[7:],
                         random_state=0)
    n = int((len(X) / 10) * 9)

    trainingX = X[:n]
    trainingY = y[:n]
    trainingD = days[:n]

    testX = X[n:]
    testY = y[n:]
    testD = days[n:]

    clf = np.array([linear_model.LinearRegression() for i in range(2)])
    clf[0].fit(trainingX[trainingD < 5], trainingY[trainingD < 5])
    clf[1].fit(trainingX[trainingD > 4], trainingY[trainingD > 4])
    return clf, testX, testY, testD
def version3():
    X, y, days = shuffle(
            reader.rain_temp_snow_prev(),
            reader.read_cyclist_data().ravel()[7:],
            reader.day_of_week()[7:],
            random_state=0)
    n = int((len(X)/10)*9)

    trainingX= X[:n]
    trainingY = y[:n]
    trainingD = days[:n]
    
    testX = X[n:]
    testY = y[n:]
    testD = days[n:]

    clf = np.array([linear_model.LinearRegression() for i in range(2)])
    clf[0].fit(trainingX[trainingD < 5], trainingY[trainingD < 5])
    clf[1].fit(trainingX[trainingD > 4], trainingY[trainingD > 4])
    return clf, testX, testY, testD