eponkratova

    def __efficiency(self, unit):
        """
        Efficiency function with already computed weights
        :param unit: which unit to compute for
        :return: efficiency
        """
        # compute efficiency
        denominator = np.dot(self.inputs, self.input_w)
        numerator = np.dot(self.outputs, self.output_w)
        return (numerator/denominator)[unit]

eponkratova

class DEA(object):
    random.seed(5)
    def __init__(self, inputs, outputs):
        """
        Initialize the DEA object with input data
        n = number of entities (observations)
        m = number of inputs (variables, features)
        r = number of outputs
        :param inputs: inputs, n x m numpy array
        :param outputs: outputs, n x r numpy array

eponkratova

+--------+-------+-------+-------+-------+----+-------+-------+-------+--+
| Branch |  x1   |  x2   |  x3   |  x4   | x5 |  y1   |  y2   |  y3   |  |
+--------+-------+-------+-------+-------+----+-------+-------+-------+--+
| A      | 86.13 | 16.24 | 48.21 | 49.69 |  9 | 54.53 | 58.98 | 38.16 |  |
| B      | 29.26 | 10.24 | 41.96 | 40.65 |  5 | 24.69 | 33.89 | 26.02 |  |
| C      | 43.12 | 11.31 | 38.19 | 35.03 |  9 | 36.41 | 40.62 | 28.51 |  |
+--------+-------+-------+-------+-------+----+-------+-------+-------+--+