update Vogel's approximation method
This commit is contained in:
Ilya Grigorev
@@ -25,8 +25,6 @@ class Result:
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self.solution = solution
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def NorthwestCorner(S: np.array,
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C: np.array,
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D: np.array) -> Result:
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@@ -50,166 +48,69 @@ def NorthwestCorner(S: np.array,
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return Result(State.UNAPPLICABLE)
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#SAMPLE INPUT FOR TESTING
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S = np.array([50, 60, 50, 50])
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C = np.array([
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[16, 16, 13, 22, 17],
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[14, 14, 13, 19, 15],
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[19, 19, 20, 23, M ],
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[M, 0, M, 0, 0]])
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D = np.array([30, 20, 70, 30, 60])
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def Vogel(
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S: np.array,
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C: np.array,
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D: np.array) -> Result:
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remaining_rows = np.ones(C.shape[0], dtype=bool)
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remaining_cols = np.ones(C.shape[1], dtype=bool)
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x_0 = np.zeros(C.shape, dtype=np.int64)
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iteration = 0
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C_initial = C
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C_init_height = len(C)
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C_init_length = len(C[0])
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solution_matrix = np.zeros((C_init_height, C_init_length), dtype=np.int64)
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print(solution_matrix)
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def add_to_solutions(val, x, y):
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for yi in range(C_init_height):
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for xi in range(C_init_length):
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if (yi == y and xi == x):
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solution_matrix[y][x] = val
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C_numerated = np.zeros((C_init_height, C_init_length), dtype=np.int64)
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C_numerated = np.insert(C_numerated, 0, [i+1 for i in range( C_init_length)], axis=0)
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C_numerated = np.insert(C_numerated, 0, [i for i in range( C_init_height+1)], axis=1)
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print(C_numerated)
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while (len(C[0]) > 1 and len(C) > 1):
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while True:
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iteration += 1
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mask = np.outer(remaining_rows, remaining_cols)
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C_length = len(C[0])
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C_height = len(C)
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RowD = np.array
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ColD = np.array
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RowD = np.resize(RowD, C_height)
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ColD = np.resize(ColD, C_length)
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if iteration > 1000:
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return Result(State.UNAPPLICABLE)
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# Finding differences
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for y in range(C_height):
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RowD[y] = abs(sorted(C[y])[0] - sorted(C[y])[1])
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for x in range(C_length):
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ColD[x] = abs(sorted(C.T[x])[0] - sorted(C.T[x])[1])
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_C = np.sort(np.where(mask, C.copy(), M*M))
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RowD = _C[:, 1] - _C[:, 0]
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_C = np.sort(np.where(mask.T, C.copy().T, M*M))
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ColD = _C[:, 1] - _C[:, 0]
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# Maximum difference
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maxD = max(np.concatenate((ColD, RowD)))
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target_array = None
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target_number = None
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row_index_to_eleminate = None
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column_index_to_eleminate = None
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x_num = None
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y_num = None
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if maxD in RowD:
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y = np.where(RowD == maxD)[0][0]
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target_array = C[np.where(RowD == maxD)[0]][0]
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target_number = min(target_array)
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x = np.where(target_array == target_number)[0][0]
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x = np.argmax(RowD, axis=0)
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y = np.argmin(np.where(mask, C.copy(), M)[x], axis=0)
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x_num = C_numerated[y+1][0]
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y_num = C_numerated[0][x+1]
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if (D[x] >= S[y]):
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row_index_to_eleminate = y
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selected_value = S[y]
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D[x] -= selected_value
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C = np.delete(C, row_index_to_eleminate, 0)
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S = np.delete(S, row_index_to_eleminate, 0)
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C_numerated = np.delete(C_numerated, row_index_to_eleminate+1, 0)
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if (D[y] == 0):
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break
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if (D[y] >= S[x]):
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selected_value = S[x]
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D[y] -= selected_value
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S[x] = 0
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remaining_rows[x] = 0
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else:
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column_index_to_eleminate = x
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selected_value = D[x]
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S[y] -= selected_value
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C = np.delete(C, column_index_to_eleminate, 1)
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D = np.delete(D, column_index_to_eleminate, 0)
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C_numerated = np.delete(C_numerated, column_index_to_eleminate+1, 1)
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selected_value = D[y]
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S[x] -= selected_value
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D[y] = 0
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remaining_cols[y] = 0
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print(x, y)
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x_0[x][y] = selected_value
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if maxD in ColD:
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x = np.where(ColD == maxD)[0][0]
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target_array = C.T[np.where(ColD == maxD)[0]][0]
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target_number = min(target_array)
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y = np.where(target_array == target_number)[0][0]
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print(ColD[x], target_array[y])
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y = np.argmax(ColD, axis=0)
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x = np.argmin(np.where(mask, C.copy(), M)[:, y], axis=0)
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x_num = C_numerated[y+1][0]
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y_num = C_numerated[0][x+1]
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if (D[y] == 0):
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break
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if (D[x] >= S[y]):
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row_index_to_eleminate = y
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selected_value = S[y]
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D[x] -= selected_value
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C = np.delete(C, row_index_to_eleminate, 0)
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S = np.delete(S, row_index_to_eleminate, 0)
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C_numerated = np.delete(C_numerated, row_index_to_eleminate+1, 0)
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if (D[y] >= S[x]):
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selected_value = S[x]
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D[y] -= selected_value
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S[x] = 0
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remaining_rows[x] = 0
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else:
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column_index_to_eleminate = x
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selected_value = D[x]
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S[y] -= selected_value
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C = np.delete(C, column_index_to_eleminate, 1)
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D = np.delete(D, column_index_to_eleminate, 0)
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C_numerated = np.delete(C_numerated, column_index_to_eleminate+1, 1)
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print(f"x_num: {x_num}, y_num: {y_num}")
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add_to_solutions(selected_value, x_num-1, y_num-1)
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print("C")
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print(C)
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print("Numerated")
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print(C_numerated)
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print(selected_value)
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print(solution_matrix)
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Z_matrix = np.zeros((C_init_height, C_init_length), dtype=np.int64)
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for y in range(C_init_height):
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for x in range(C_init_length):
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Z_matrix[y][x] = solution_matrix[y][x] * C_initial[y][x]
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Z = np.sum(Z_matrix)
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print(f"Z = {Z}")
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result = Result(State.SOLVED, Z, solution_matrix)
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return result
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''' if target_array is not None:
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objective_function_value = np.sum(np.dot(C, target_array))
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return Result(State.SOLVED, objective_function_value, target_array)
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else:
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return Result(State.UNAPPLICABLE)'''
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Vogel(S,C,D)
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selected_value = D[y]
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S[x] -= selected_value
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D[y] = 0
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remaining_cols[y] = 0
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x_0[x][y] = selected_value
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return Result(State.SOLVED, C * x_0, x_0)
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def Russell(
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@@ -259,7 +160,6 @@ def Russell(
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return Result(State.SOLVED, C * x_0, x_0)
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def print_problem_statement(
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S: np.array,
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C: np.array,
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@@ -285,7 +185,6 @@ def print_problem_statement(
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table += "\n" + "_ " * ((len(matrix[0])-1) * 2)
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table += f"\n{row}"
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print(table)
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print(print_problem_statement(S,C,D))
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def solve(
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@@ -372,11 +271,12 @@ def TEST_CASE_1():
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return solve(S, C, D, NWExpected, VogelExpected, RussellExpected)
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def TEST_CASE_2():
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print("----------------------RUNNING_TEST_CASE_2----------------------")
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C = np.array([[5, 8, 6],
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[4, 7, 9],
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[3, 8, 5]], dtype=np.int64)
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[4, 7, 9],
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[3, 8, 5]], dtype=np.int64)
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S = np.array([20, 30, 25], dtype=np.int64)
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@@ -390,9 +290,9 @@ def TEST_CASE_2():
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], dtype=np.int64)
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VogelExpected = np.array([
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[0],
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[0],
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[0] # Заполнить
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[0, 5, 15],
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[10, 20, 0],
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[0, 0, 25]
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], dtype=np.int64)
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RussellExpected = np.array([
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@@ -1,24 +1,19 @@
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from main import Vogel
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from main import Vogel, M
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import numpy as np
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C = np.array([
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[7, 8, 1, 2],
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[4, 5, 9, 8],
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[9, 2, 3, 6],
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[16, 16, 13, 22, 17],
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[14, 14, 13, 19, 15],
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[19, 19, 20, 23, M],
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[M, 0, M, 0, 0]
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], dtype=np.int64)
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S = np.array([
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160, 140, 170
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50, 60, 50, 50
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], dtype=np.int64)
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D = np.array([
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120, 50, 190, 110
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], dtype=np.int64)
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VogelExpected = np.array([
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[0, 0, 50, 110],
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[120, 20, 0, 0],
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[0, 30, 140, 0],
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30, 20, 70, 30, 60
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], dtype=np.int64)
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print(Vogel(S, C, D).solution)
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