import numpy as np
from predict.model.interfaces import IModel


class PolyFit(IModel):

    def __init__(self, name, inputlen=7, outputlen=1):
        IModel.__init__(self, 'poly', name)
        self.__InputLength = inputlen
        self.__OutputLength = outputlen

    def fit(self, x, y):
        self.updatecheckpoint()
        pass

    def inputrequire(self):
        return self.__InputLength

    def maxpredict(self):
        return self.__OutputLength

    def predict(self, x):
        """
            Predict each dimension separately
        """
        x = np.array(x)
        t = np.arange(0, self.__InputLength, 1)
        t_pred = np.arange(self.__InputLength, self.__InputLength + self.
            __OutputLength, 1)
        pred = []
        for dim in range(x.shape[1]):
            f = np.polyfit(t, x[-self.__InputLength:, dim], 3)
            poly3 = np.poly1d(f)
            pred.append(poly3(t_pred))
        pred = np.asarray(pred).transpose()
        return pred

897f534c91b560910b6740b1996f007153cd1149
<mask token>


class PolyFit(IModel):
    <mask token>
    <mask token>

    def inputrequire(self):
        return self.__InputLength

    def maxpredict(self):
        return self.__OutputLength

    def predict(self, x):
        """
            Predict each dimension separately
        """
        x = np.array(x)
        t = np.arange(0, self.__InputLength, 1)
        t_pred = np.arange(self.__InputLength, self.__InputLength + self.
            __OutputLength, 1)
        pred = []
        for dim in range(x.shape[1]):
            f = np.polyfit(t, x[-self.__InputLength:, dim], 3)
            poly3 = np.poly1d(f)
            pred.append(poly3(t_pred))
        pred = np.asarray(pred).transpose()
        return pred

<mask token>


class PolyFit(IModel):
    <mask token>

    def fit(self, x, y):
        self.updatecheckpoint()
        pass

    def inputrequire(self):
        return self.__InputLength

    def maxpredict(self):
        return self.__OutputLength

    def predict(self, x):
        """
            Predict each dimension separately
        """
        x = np.array(x)
        t = np.arange(0, self.__InputLength, 1)
        t_pred = np.arange(self.__InputLength, self.__InputLength + self.
            __OutputLength, 1)
        pred = []
        for dim in range(x.shape[1]):
            f = np.polyfit(t, x[-self.__InputLength:, dim], 3)
            poly3 = np.poly1d(f)
            pred.append(poly3(t_pred))
        pred = np.asarray(pred).transpose()
        return pred

<mask token>


class PolyFit(IModel):

    def __init__(self, name, inputlen=7, outputlen=1):
        IModel.__init__(self, 'poly', name)
        self.__InputLength = inputlen
        self.__OutputLength = outputlen

    def fit(self, x, y):
        self.updatecheckpoint()
        pass

    def inputrequire(self):
        return self.__InputLength

    def maxpredict(self):
        return self.__OutputLength

    def predict(self, x):
        """
            Predict each dimension separately
        """
        x = np.array(x)
        t = np.arange(0, self.__InputLength, 1)
        t_pred = np.arange(self.__InputLength, self.__InputLength + self.
            __OutputLength, 1)
        pred = []
        for dim in range(x.shape[1]):
            f = np.polyfit(t, x[-self.__InputLength:, dim], 3)
            poly3 = np.poly1d(f)
            pred.append(poly3(t_pred))
        pred = np.asarray(pred).transpose()
        return pred

import numpy as np
from predict.model.interfaces import IModel


class PolyFit(IModel):

    def __init__(self, name, inputlen=7, outputlen=1):
        IModel.__init__(self, 'poly', name)
        self.__InputLength = inputlen
        self.__OutputLength = outputlen

    def fit(self, x, y):
        self.updatecheckpoint()
        pass

    def inputrequire(self):
        return self.__InputLength

    def maxpredict(self):
        return self.__OutputLength

    def predict(self, x):
        """
            Predict each dimension separately
        """
        x = np.array(x)
        t = np.arange(0, self.__InputLength, 1)
        t_pred = np.arange(self.__InputLength, self.__InputLength + self.
            __OutputLength, 1)
        pred = []
        for dim in range(x.shape[1]):
            f = np.polyfit(t, x[-self.__InputLength:, dim], 3)
            poly3 = np.poly1d(f)
            pred.append(poly3(t_pred))
        pred = np.asarray(pred).transpose()
        return pred
