def map_fit(interface, state, label, inp):
    """
    Function calculates matrices ete and etde for every sample, aggregates and output them.
    """
    import numpy as np
    ete, etde = 0, 0
    out = interface.output(0)

    for row in inp:
        row = row.strip().split(state["delimiter"])  # split row
        if len(row) > 1:  # check if row is empty
            # intercept term is added to every sample
            x = np.array([(0 if v in state["missing_vals"] else float(v)) for i, v in enumerate(row) if
                          i in state["X_indices"]] + [-1])
            # map label value to 1 or -1. If label does not match set error
            y = 1 if state["y_map"][0] == row[state["y_index"]] else -1 if state["y_map"][1] == row[
                state["y_index"]] else "Error"
            ete += np.outer(x, x)
            etde += x * y
    out.add("etde", etde)
    for i, row in enumerate(ete):
        out.add(i, row)
Function calculates matrices ete and etde for every sample, aggregates and output them.