def get_matrix_cost(matrix: np.matrix) -> np.matrix:
cost_matrix = matrix.copy()
minimal_column = matrix.min(1)[:np.newaxis]
minimal_column[minimal_column == np.inf] = 0
# Subtract minimal value of a row from matrix
cost_matrix -= minimal_column
minimal_row = cost_matrix.min(0)
minimal_row[minimal_row == np.inf] = 0
# Subtract minimal value of a column from matrix
cost_matrix -= minimal_row
return cost_matrix
def __init__(self, vals: np.matrix):
self.shift_factor = np.array(vals.min(1)).astype(float)
self.scale_factor = np.array(vals.max(1) -
self.shift_factor).astype(float)
self.scale_factor = self.scale_factor + (self.scale_factor
== 0).astype(float)
self.shift_factor = self.shift_factor.reshape(
self.shift_factor.shape[0], 1)
self.scale_factor = self.scale_factor.reshape(
self.scale_factor.shape[0], 1)
self.scale_factor = 1.0
def get_matrix_cost(matrix: np.matrix, debug=False) -> tuple:
matrix_mod = matrix.copy()
minimal_column = matrix.min(1)[:np.newaxis]
minimal_column[minimal_column == np.inf] = 0
cost = np.sum(minimal_column)
# Subtract minimal value of a row from matrix
matrix_mod -= minimal_column
minimal_row = matrix_mod.min(0)
minimal_row[minimal_row == np.inf] = 0
cost += np.sum(minimal_row)
# Subtract minimal value of a column from matrix
matrix_mod -= minimal_row
if debug:
print(f"Minimal column: {minimal_column}")
print(f"Minimal column cost: {np.sum(minimal_column)}")
print(f"Minimal row: {minimal_row}")
print(f"Minimal row cost: {np.sum(minimal_row)}")
return cost, matrix_mod
def ahp(item_no: np.matrix, m: np.matrix, w: np.matrix):
"""
ABC analysis according to Analytical Hierarchy Process (AHP)
Arguments:
item_no {[type]} -- list of item numbers
m {[type]} -- ranking criteria
Returns:
[type] -- a matrix of item ranked and classified according to AHP
"""
fmin, fmax = m.min(0), m.max(0)
norm = lambda i, v : (v-fmin[0,i])/(fmax[0,i]-fmin[0,i])
ahp = [[item_no[k,0],sum(w[i]*norm(i, m[k,i]) for i in range(0,4))] for k in range(0,len(m))]
ahp = sorted(ahp,key=lambda x: x[1], reverse=True)
# annotate ABC
ABC.annotate_ABC(ahp)
return ahp
def bounding_box(points: np.matrix):
"""Return a, b so that all points lay in bounding box spanned by a and b."""
return points.min(axis=0), points.max(axis=0)