Python use every element in ndarray as param for lambda function

Question

I'm trying to calculate the vector basis for any transformation in R^n -> R^m.
In order to achieve this I wrote lamba functions to represent the actual function. Some samples are:

R2 -> R2: g1 = lambda x,y: np.array([2*x+y, x+2*y])
R3 -> R1: g3 = lambda x,y,z: np.array([x, -2*y, 3*z])

To have a function doing my work I came up with this:

def calculate_Vector_Basis(f, numberOfArgs):
    """
    Calculates the result for every base vector ex, ey, ...
    Params:
        f : function with generic number of arguments
        numberOfArgs: The number of arguments for the generic function
    Returns:
        [] : Array of base vectors
    """

    # Collection of base vectors
    vector_basis = []
    for i in range(numberOfArgs):
        # Create unit vector e with zeros only
        base = np.zeros(numberOfArgs)
        # Set 1 where at the axis required (for R3: x = [1 0 0], y = [0 1 0], z = [0 0 1])
        base[i] = 1
        # Call function f for every unit vector e
        vector_basis.append(f(base[0], base[1]))
    return vector_basis

The function should iterate and create unit vectors according to the dimension of the given n-dimensonal room of the rational numbers.
Samples:

R2 = [1 0], [0 1]
R3 = [1 0 0], [0 1 0], [0 0 1]

I'm stuck where I want to call the passed in lambda function f.
Based on f's definition I need 1 up to 5 params.
For R2 I'd have to call f(base[0], base[1]) in R3 I'd have to call f(base[0], base[1], base[2]). base is an ndarray that looks like this [xval, yval, ...]. Is there a possibility to call f with every value in the ndarray?

javidcf · Accepted Answer · 2019-05-24 10:35:52Z

2

Yes, can do that with argument unpacking:

def calculate_Vector_Basis(f, numberOfArgs):
    """
    Calculates the result for every base vector ex, ey, ...
    Params:
        f : function with generic number of arguments
        numberOfArgs: The number of arguments for the generic function
    Returns:
        [] : Array of base vectors
    """

    # Collection of base vectors
    vector_basis = []
    for i in range(numberOfArgs):
        # Create unit vector e with zeros only
        base = np.zeros(numberOfArgs)
        # Set 1 where at the axis required (for R3: x = [1 0 0], y = [0 1 0], z = [0 0 1])
        base[i] = 1
        # Call function f for every unit vector e
        vector_basis.append(f(*base))
    return vector_basis

answered May 24, 2019 at 10:35

javidcf

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