def input_from_history(a, n, bias=False):
    """
    This is function for creation of input matrix.

    **Args:**

    * `a` : series (1 dimensional array)

    * `n` : size of input matrix row (int). It means how many samples \
        of previous history you want to use \
        as the filter input. It also represents the filter length.

    **Kwargs:**

    * `bias` : decides if the bias is used (Boolean). If True, \
        array of all ones is appended as a last column to matrix `x`. \
        So matrix `x` has `n`+1 columns.

    **Returns:**

    * `x` : input matrix (2 dimensional array) \
        constructed from an array `a`. The length of `x` \
        is calculated as length of `a` - `n` + 1. \
        If the `bias` is used, then the amount of columns is `n` if not then \
        amount of columns is `n`+1).

    """
    if not type(n) == int:
        raise ValueError('The argument n must be int.')
    if not n > 0:
        raise ValueError('The argument n must be greater than 0')
    try:
        a = np.array(a, dtype="float64")
    except:
        raise ValueError('The argument a is not numpy array or similar.')
    x = np.array([a[i:i+n] for i in range(len(a)-n+1)]) 
    if bias:
        x = np.vstack((x.T, np.ones(len(x)))).T
    return x
def input_from_history ( a , n , bias = False ) : if not type ( n ) == int : raise ValueError ( 'The argument n must be int.' ) if not n > 0 : raise ValueError ( 'The argument n must be greater than 0' ) try : a = np . array ( a , dtype = "float64" ) except : raise ValueError ( 'The argument a is not numpy array or similar.' ) x = np . array ( [ a [ i : i + n ] for i in range ( len ( a ) - n + 1 ) ] ) if bias : x = np . vstack ( ( x . T , np . ones ( len ( x ) ) ) ) . T return x
This is function for creation of input matrix.

    **Args:**

    * `a` : series (1 dimensional array)

    * `n` : size of input matrix row (int). It means how many samples \
        of previous history you want to use \
        as the filter input. It also represents the filter length.

    **Kwargs:**

    * `bias` : decides if the bias is used (Boolean). If True, \
        array of all ones is appended as a last column to matrix `x`. \
        So matrix `x` has `n`+1 columns.

    **Returns:**

    * `x` : input matrix (2 dimensional array) \
        constructed from an array `a`. The length of `x` \
        is calculated as length of `a` - `n` + 1. \
        If the `bias` is used, then the amount of columns is `n` if not then \
        amount of columns is `n`+1).