def run(self, d, x):
        """
        This function filters multiple samples in a row.

        **Args:**

        * `d` : desired value (1 dimensional array)

        * `x` : input matrix (2-dimensional array). Rows are samples,
          columns are input arrays.

        **Returns:**

        * `y` : output value (1 dimensional array).
          The size corresponds with the desired value.

        * `e` : filter error for every sample (1 dimensional array).
          The size corresponds with the desired value.

        * `w` : history of all weights (2 dimensional array).
          Every row is set of the weights for given sample.
        """
        # measure the data and check if the dimmension agree
        N = len(x)
        if not len(d) == N:
            raise ValueError('The length of vector d and matrix x must agree.')  
        self.n = len(x[0])
        # prepare data
        try:    
            x = np.array(x)
            d = np.array(d)
        except:
            raise ValueError('Impossible to convert x or d to a numpy array')
        # create empty arrays
        y = np.zeros(N)
        e = np.zeros(N)
        self.w_history = np.zeros((N,self.n))
        # adaptation loop
        for k in range(N):
            self.w_history[k,:] = self.w
            y[k] = np.dot(self.w, x[k])
            e[k] = d[k] - y[k]
            nu = self.mu / (self.eps + np.dot(x[k], x[k]))
            dw = nu * x[k] * e[k]**3
            self.w += dw
        return y, e, self.w_history
This function filters multiple samples in a row.

        **Args:**

        * `d` : desired value (1 dimensional array)

        * `x` : input matrix (2-dimensional array). Rows are samples,
          columns are input arrays.

        **Returns:**

        * `y` : output value (1 dimensional array).
          The size corresponds with the desired value.

        * `e` : filter error for every sample (1 dimensional array).
          The size corresponds with the desired value.

        * `w` : history of all weights (2 dimensional array).
          Every row is set of the weights for given sample.