Apply yapf on examples/python/*.py
This commit is contained in:
Corentin Le Molgat
@@ -133,11 +133,10 @@ def get_optimal_schedule(demand, args):
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"""Computes the optimal schedule for the appointment selection problem."""
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combinations = find_combinations([a[2] for a in demand], args.load_min,
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args.load_max, args.commute_time)
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print(
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'found %d possible combinations of appointements' % len(combinations))
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print('found %d possible combinations of appointements' % len(combinations))
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cost, selection = select(combinations, [a[0] for a in demand],
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args.num_workers)
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cost, selection = select(combinations, [a[0]
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for a in demand], args.num_workers)
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output = [(selection[i], [(combinations[i][t], demand[t][1])
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for t in range(len(demand))
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if combinations[i][t] != 0])
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@@ -152,8 +151,7 @@ def main(args):
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for a in demand:
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print(' %d * %s : %d min' % (a[0], a[1], a[2]))
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print('commute time = %d' % args.commute_time)
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print(
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'accepted total duration = [%d..%d]' % (args.load_min, args.load_max))
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print('accepted total duration = [%d..%d]' % (args.load_min, args.load_max))
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print('%d workers' % args.num_workers)
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cost, selection = get_optimal_schedule(demand, args)
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print('Optimal solution as a cost of %d' % cost)
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@@ -17,16 +17,15 @@ from ortools.sat.python import cp_model
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def main():
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# Instantiate a cp model.
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cost = [[90, 76, 75, 70, 50, 74, 12,
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68], [35, 85, 55, 65, 48, 101, 70, 83],
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[125, 95, 90, 105, 59, 120, 36, 73],
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[45, 110, 95, 115, 104, 83, 37, 71],
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[60, 105, 80, 75, 59, 62, 93,
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88], [45, 65, 110, 95, 47, 31, 81, 34],
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[38, 51, 107, 41, 69, 99, 115, 48],
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[47, 85, 57, 71, 92, 77, 109, 36],
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[39, 63, 97, 49, 118, 56, 92, 61],
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[47, 101, 71, 60, 88, 109, 52, 90]]
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cost = [[90, 76, 75, 70, 50, 74, 12, 68], [35, 85, 55, 65, 48, 101, 70, 83],
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[125, 95, 90, 105, 59,
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120, 36, 73], [45, 110, 95, 115, 104, 83, 37,
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71], [60, 105, 80, 75, 59, 62, 93,
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88], [45, 65, 110, 95, 47, 31, 81, 34],
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[38, 51, 107, 41, 69, 99, 115,
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48], [47, 85, 57, 71, 92, 77, 109,
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36], [39, 63, 97, 49, 118, 56,
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92, 61], [47, 101, 71, 60, 88, 109, 52, 90]]
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sizes = [10, 7, 3, 12, 15, 4, 11, 5]
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total_size_max = 15
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@@ -55,8 +54,8 @@ def main():
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model.Add(sum(sizes[j] * x[i][j] for j in all_tasks) <= total_size_max)
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# Total cost
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model.Add(total_cost == sum(
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x[i][j] * cost[i][j] for j in all_tasks for i in all_workers))
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model.Add(total_cost == sum(x[i][j] * cost[i][j]
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for j in all_tasks for i in all_workers))
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model.Minimize(total_cost)
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solver = cp_model.CpSolver()
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@@ -20,12 +20,14 @@ from ortools.sat.python import cp_model
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def solve_assignment():
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"""Solve the assignment problem."""
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# Data.
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cost = [[90, 76, 75, 70, 50, 74], [35, 85, 55, 65, 48, 101],
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[125, 95, 90, 105, 59, 120], [45, 110, 95, 115, 104, 83],
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[60, 105, 80, 75, 59, 62], [45, 65, 110, 95, 47, 31],
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[38, 51, 107, 41, 69, 99], [47, 85, 57, 71, 92, 77],
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[39, 63, 97, 49, 118, 56], [47, 101, 71, 60, 88, 109],
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[17, 39, 103, 64, 61, 92], [101, 45, 83, 59, 92, 27]]
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cost = [[90, 76, 75, 70, 50, 74], [35, 85, 55, 65, 48,
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101], [125, 95, 90, 105, 59, 120],
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[45, 110, 95, 115, 104, 83], [60, 105, 80, 75, 59, 62], [
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45, 65, 110, 95, 47, 31
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], [38, 51, 107, 41, 69, 99], [47, 85, 57, 71,
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92, 77], [39, 63, 97, 49, 118, 56],
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[47, 101, 71, 60, 88, 109], [17, 39, 103, 64, 61,
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92], [101, 45, 83, 59, 92, 27]]
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group1 = [
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[0, 0, 1, 1], # Workers 2, 3
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@@ -79,8 +81,7 @@ def solve_assignment():
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# Total task size for each worker is at most total_size_max
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for i in all_workers:
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model.Add(
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sum(sizes[j] * selected[i][j]
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for j in all_tasks) <= total_size_max)
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sum(sizes[j] * selected[i][j] for j in all_tasks) <= total_size_max)
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# Group constraints.
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model.AddAllowedAssignments([works[0], works[1], works[2], works[3]],
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@@ -102,8 +102,8 @@ def main():
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item_in_group = {}
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for i in all_items:
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for g in all_groups:
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item_in_group[(i, g)] = model.NewBoolVar(
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'item %d in group %d' % (i, g))
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item_in_group[(i, g)] = model.NewBoolVar('item %d in group %d' %
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(i, g))
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# Each group must have the same size.
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for g in all_groups:
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@@ -30,9 +30,9 @@ max_quantities = [["N_Total", 1944], ["P2O5", 1166.4], ["K2O", 1822.5],
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["CaO", 1458], ["MgO", 486], ["Fe", 9.7], ["B", 2.4]]
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chemical_set = [["A", 0, 0, 510, 540, 0, 0, 0], ["B", 110, 0, 0, 0, 160, 0, 0],
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["C", 61, 149, 384, 0, 30, 1, 0.2],
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["D", 148, 70, 245, 0, 15, 1, 0.2],
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["E", 160, 158, 161, 0, 10, 1, 0.2]]
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["C", 61, 149, 384, 0, 30, 1,
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0.2], ["D", 148, 70, 245, 0, 15, 1,
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0.2], ["E", 160, 158, 161, 0, 10, 1, 0.2]]
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num_products = len(max_quantities)
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all_products = range(num_products)
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@@ -87,6 +87,6 @@ for s in all_sets:
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for p in all_products:
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name = max_quantities[p][0]
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max_quantity = max_quantities[p][1]
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quantity = sum(set_vars[s].solution_value() * chemical_set[s][p + 1]
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for s in all_sets)
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quantity = sum(
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set_vars[s].solution_value() * chemical_set[s][p + 1] for s in all_sets)
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print("%s: %f out of %f" % (name, quantity, max_quantity))
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@@ -29,9 +29,9 @@ max_quantities = [["N_Total", 1944], ["P2O5", 1166.4], ["K2O", 1822.5],
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["CaO", 1458], ["MgO", 486], ["Fe", 9.7], ["B", 2.4]]
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chemical_set = [["A", 0, 0, 510, 540, 0, 0, 0], ["B", 110, 0, 0, 0, 160, 0, 0],
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["C", 61, 149, 384, 0, 30, 1, 0.2],
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["D", 148, 70, 245, 0, 15, 1, 0.2],
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["E", 160, 158, 161, 0, 10, 1, 0.2]]
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["C", 61, 149, 384, 0, 30, 1,
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0.2], ["D", 148, 70, 245, 0, 15, 1,
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0.2], ["E", 160, 158, 161, 0, 10, 1, 0.2]]
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num_products = len(max_quantities)
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all_products = range(num_products)
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@@ -76,8 +76,8 @@ def create_data_model():
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#######################
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def manhattan_distance(position_1, position_2):
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"""Computes the Manhattan distance between two points"""
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return (abs(position_1[0] - position_2[0]) +
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abs(position_1[1] - position_2[1]))
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return (
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abs(position_1[0] - position_2[0]) + abs(position_1[1] - position_2[1]))
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def create_distance_evaluator(data):
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@@ -114,8 +114,8 @@ def create_data_model():
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#######################
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def manhattan_distance(position_1, position_2):
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"""Computes the Manhattan distance between two points"""
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return (abs(position_1[0] - position_2[0]) +
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abs(position_1[1] - position_2[1]))
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return (
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abs(position_1[0] - position_2[0]) + abs(position_1[1] - position_2[1]))
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def create_distance_evaluator(data):
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@@ -198,9 +198,8 @@ def create_time_evaluator(data):
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if from_node == to_node:
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travel_time = 0
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else:
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travel_time = manhattan_distance(
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data['locations'][from_node],
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data['locations'][to_node]) / data['vehicle_speed']
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travel_time = manhattan_distance(data['locations'][from_node], data[
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'locations'][to_node]) / data['vehicle_speed']
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return travel_time
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_total_time = {}
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@@ -212,8 +211,8 @@ def create_time_evaluator(data):
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_total_time[from_node][to_node] = 0
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else:
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_total_time[from_node][to_node] = int(
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service_time(data, from_node) +
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travel_time(data, from_node, to_node))
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service_time(data, from_node) + travel_time(
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data, from_node, to_node))
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def time_evaluator(manager, from_node, to_node):
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"""Returns the total time between the two nodes"""
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@@ -279,7 +278,8 @@ def print_solution(data, manager, routing, assignment): # pylint:disable=too-ma
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load_var = capacity_dimension.CumulVar(index)
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time_var = time_dimension.CumulVar(index)
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plan_output += ' {0} Load({1}) Time({2},{3}) ->'.format(
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manager.IndexToNode(index), assignment.Value(load_var),
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manager.IndexToNode(index),
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assignment.Value(load_var),
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assignment.Min(time_var), assignment.Max(time_var))
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previous_index = index
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index = assignment.Value(routing.NextVar(index))
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@@ -288,7 +288,8 @@ def print_solution(data, manager, routing, assignment): # pylint:disable=too-ma
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load_var = capacity_dimension.CumulVar(index)
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time_var = time_dimension.CumulVar(index)
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plan_output += ' {0} Load({1}) Time({2},{3})\n'.format(
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manager.IndexToNode(index), assignment.Value(load_var),
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manager.IndexToNode(index),
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assignment.Value(load_var),
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assignment.Min(time_var), assignment.Max(time_var))
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plan_output += 'Distance of the route: {}m\n'.format(distance)
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plan_output += 'Load of the route: {}\n'.format(
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@@ -88,8 +88,8 @@ def create_data_model():
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#######################
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def manhattan_distance(position_1, position_2):
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"""Computes the Manhattan distance between two points"""
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return (abs(position_1[0] - position_2[0]) +
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abs(position_1[1] - position_2[1]))
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return (
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abs(position_1[0] - position_2[0]) + abs(position_1[1] - position_2[1]))
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def create_distance_evaluator(data):
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@@ -147,9 +147,8 @@ def create_time_evaluator(data):
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if from_node == to_node:
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travel_time = 0
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else:
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travel_time = manhattan_distance(
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data['locations'][from_node],
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data['locations'][to_node]) / data['vehicle_speed']
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travel_time = manhattan_distance(data['locations'][from_node], data[
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'locations'][to_node]) / data['vehicle_speed']
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return travel_time
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_total_time = {}
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@@ -161,8 +160,8 @@ def create_time_evaluator(data):
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_total_time[from_node][to_node] = 0
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else:
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_total_time[from_node][to_node] = int(
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service_time(data, from_node) +
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travel_time(data, from_node, to_node))
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service_time(data, from_node) + travel_time(
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data, from_node, to_node))
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def time_evaluator(manager, from_node, to_node):
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"""Returns the total time between the two nodes"""
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@@ -222,8 +221,10 @@ def print_solution(data, manager, routing, assignment): # pylint:disable=too-ma
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time_var = time_dimension.CumulVar(index)
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slack_var = time_dimension.SlackVar(index)
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plan_output += ' {0} Load({1}) Time({2},{3}) Slack({4},{5}) ->'.format(
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manager.IndexToNode(index), assignment.Value(load_var),
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assignment.Min(time_var), assignment.Max(time_var),
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manager.IndexToNode(index),
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assignment.Value(load_var),
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assignment.Min(time_var),
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assignment.Max(time_var),
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assignment.Min(slack_var), assignment.Max(slack_var))
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previous_index = index
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index = assignment.Value(routing.NextVar(index))
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@@ -233,7 +234,8 @@ def print_solution(data, manager, routing, assignment): # pylint:disable=too-ma
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time_var = time_dimension.CumulVar(index)
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slack_var = time_dimension.SlackVar(index)
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plan_output += ' {0} Load({1}) Time({2},{3})\n'.format(
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manager.IndexToNode(index), assignment.Value(load_var),
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manager.IndexToNode(index),
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assignment.Value(load_var),
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assignment.Min(time_var), assignment.Max(time_var))
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plan_output += 'Distance of the route: {0}m\n'.format(distance)
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plan_output += 'Load of the route: {}\n'.format(
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@@ -92,10 +92,10 @@ class Customers():
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self.extents = extents #: The lower left and upper right points
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#: Location[lat,lon]: the centre point of the area.
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self.center = Location(
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extents['urcrnrlat'] -
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0.5 * (extents['urcrnrlat'] - extents['llcrnrlat']),
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extents['urcrnrlon'] -
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0.5 * (extents['urcrnrlon'] - extents['llcrnrlon']))
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extents['urcrnrlat'] - 0.5 *
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(extents['urcrnrlat'] - extents['llcrnrlat']),
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extents['urcrnrlon'] - 0.5 *
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(extents['urcrnrlon'] - extents['llcrnrlon']))
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else:
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#: Location[lat,lon]: the centre point of the area.
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(clat, clon) = self.center = Location(center[0], center[1])
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@@ -103,14 +103,12 @@ class Customers():
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circ_earth = np.pi * rad_earth
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#: The lower left and upper right points
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self.extents = {
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'llcrnrlon':
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(clon -
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180 * box_size / (circ_earth * np.cos(np.deg2rad(clat)))),
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'llcrnrlon': (clon - 180 * box_size /
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(circ_earth * np.cos(np.deg2rad(clat)))),
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'llcrnrlat':
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clat - 180 * box_size / circ_earth,
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'urcrnrlon':
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(clon +
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180 * box_size / (circ_earth * np.cos(np.deg2rad(clat)))),
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'urcrnrlon': (clon + 180 * box_size /
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(circ_earth * np.cos(np.deg2rad(clat)))),
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'urcrnrlat':
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clat + 180 * box_size / circ_earth
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}
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@@ -38,9 +38,9 @@ def flexible_jobshop():
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jobs = [[[(3, 0), (1, 1), (5, 2)], [(2, 0), (4, 1), (6, 2)],
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[(2, 0), (3, 1), (1, 2)]],
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[[(2, 0), (3, 1), (4, 2)], [(1, 0), (5, 1), (4, 2)],
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[(2, 0), (1, 1), (4, 2)]],
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[[(2, 0), (1, 1), (4, 2)], [(2, 0), (3, 1), (4, 2)],
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[(3, 0), (1, 1), (5, 2)]]]
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[(2, 0), (1, 1), (4, 2)]], [[(2, 0), (1, 1),
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(4, 2)], [(2, 0), (3, 1), (4, 2)],
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[(3, 0), (1, 1), (5, 2)]]]
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num_jobs = len(jobs)
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all_jobs = range(num_jobs)
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@@ -51,8 +51,7 @@ def main():
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start = model.NewIntVar(0, horizon, 'start_%i' % i)
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duration = jobs[i][0]
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end = model.NewIntVar(0, horizon, 'end_%i' % i)
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interval = model.NewIntervalVar(start, duration, end,
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'interval_%i' % i)
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interval = model.NewIntervalVar(start, duration, end, 'interval_%i' % i)
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starts.append(start)
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intervals.append(interval)
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ends.append(end)
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@@ -114,8 +113,7 @@ def main():
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d_y = jobs[i][1]
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s_y = performed_machine * (max_length - d_y)
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output.AddRectangle(start, s_y, d_x, d_y,
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color_manager.RandomColor(), 'black',
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'j%i' % i)
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color_manager.RandomColor(), 'black', 'j%i' % i)
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output.AddXScale()
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output.AddYScale()
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@@ -126,8 +124,8 @@ def main():
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for i in all_jobs:
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performed_machine = 1 - solver.Value(performed[i])
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start = solver.Value(starts[i])
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print(' - Job %i starts at %i on machine %i' %
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(i, start, performed_machine))
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print(' - Job %i starts at %i on machine %i' % (i, start,
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performed_machine))
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print('Statistics')
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print(' - conflicts : %i' % solver.NumConflicts())
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print(' - branches : %i' % solver.NumBranches())
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@@ -42,8 +42,8 @@ def main():
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solver.Add(marks[0] == 0)
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solver.Add(
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solver.AllDifferent([
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marks[j] - marks[i] for i in range(0, size - 1)
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for j in range(i + 1, size)
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marks[j] - marks[i]
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for i in range(0, size - 1) for j in range(i + 1, size)
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]))
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solver.Add(marks[size - 1] - marks[size - 2] > marks[1] - marks[0])
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@@ -31,10 +31,13 @@ def build_pairs(rows, cols):
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rows: the number of rows in the grid
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cols: the number of columns in the grid
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"""
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return [(x * cols + y, (x + dx) * cols + (y + dy)) for x in range(rows)
|
||||
for y in range(cols) for dx in (-1, 0, 1) for dy in (-1, 0, 1)
|
||||
if (x + dx >= 0 and x + dx < rows and y + dy >= 0 and y + dy < cols
|
||||
and (dx != 0 or dy != 0))]
|
||||
return [
|
||||
(x * cols + y, (x + dx) * cols + (y + dy))
|
||||
for x in range(rows) for y in range(cols) for dx in (-1, 0, 1)
|
||||
for dy in (-1, 0, 1)
|
||||
if (x + dx >= 0 and x + dx < rows and y + dy >= 0 and y + dy < cols and
|
||||
(dx != 0 or dy != 0))
|
||||
]
|
||||
|
||||
|
||||
def print_solution(positions, rows, cols):
|
||||
@@ -80,8 +83,8 @@ def build_puzzle(problem):
|
||||
elif problem == 2:
|
||||
puzzle = [[0, 44, 41, 0, 0, 0, 0], [0, 43, 0, 28, 29, 0, 0],
|
||||
[0, 1, 0, 0, 0, 33, 0], [0, 2, 25, 4, 34, 0, 36],
|
||||
[49, 16, 0, 23, 0, 0, 0], [0, 19, 0, 0, 12, 7, 0],
|
||||
[0, 0, 0, 14, 0, 0, 0]]
|
||||
[49, 16, 0, 23, 0, 0, 0], [0, 19, 0, 0, 12, 7,
|
||||
0], [0, 0, 0, 14, 0, 0, 0]]
|
||||
|
||||
elif problem == 3:
|
||||
# Problems from the book:
|
||||
@@ -103,9 +106,10 @@ def build_puzzle(problem):
|
||||
elif problem == 6:
|
||||
# Problem 15 (Intermediate)
|
||||
puzzle = [[64, 0, 0, 0, 0, 0, 0, 0], [1, 63, 0, 59, 15, 57, 53, 0],
|
||||
[0, 4, 0, 14, 0, 0, 0, 0], [3, 0, 11, 0, 20, 19, 0, 50],
|
||||
[0, 0, 0, 0, 22, 0, 48, 40], [9, 0, 0, 32, 23, 0, 0, 41],
|
||||
[27, 0, 0, 0, 36, 0, 46, 0], [28, 30, 0, 35, 0, 0, 0, 0]]
|
||||
[0, 4, 0, 14, 0, 0, 0, 0], [3, 0, 11, 0, 20, 19, 0,
|
||||
50], [0, 0, 0, 0, 22, 0, 48, 40],
|
||||
[9, 0, 0, 32, 23, 0, 0, 41], [27, 0, 0, 0, 36, 0, 46,
|
||||
0], [28, 30, 0, 35, 0, 0, 0, 0]]
|
||||
return puzzle
|
||||
|
||||
|
||||
@@ -168,8 +172,8 @@ def solve_hidato(puzzle, index):
|
||||
output.AddRectangle(x, r - y - 1, 1, 1, color, 'black',
|
||||
str(i + 1))
|
||||
|
||||
output.AddTitle(
|
||||
'Puzzle %i solved in %f s' % (index, solver.WallTime()))
|
||||
output.AddTitle('Puzzle %i solved in %f s' % (index,
|
||||
solver.WallTime()))
|
||||
output.Display()
|
||||
else:
|
||||
print_solution(
|
||||
|
||||
@@ -80,8 +80,8 @@ def SolveAndPrint(solver, variable_list):
|
||||
|
||||
|
||||
def Announce(solver, api_type):
|
||||
print(('---- Integer programming example with ' + solver + ' (' + api_type
|
||||
+ ') -----'))
|
||||
print(('---- Integer programming example with ' + solver + ' (' + api_type +
|
||||
') -----'))
|
||||
|
||||
|
||||
def RunAllIntegerExampleNaturalLanguageAPI():
|
||||
|
||||
@@ -102,8 +102,8 @@ def jobshop_ft06_distance():
|
||||
# We add the reified precedence to link the literal with the
|
||||
# times of the two tasks.
|
||||
min_distance = distance_between_jobs(j1, j2)
|
||||
model.Add(job_starts[j2] >= job_ends[j1] +
|
||||
min_distance).OnlyEnforceIf(lit)
|
||||
model.Add(job_starts[j2] >=
|
||||
job_ends[j1] + min_distance).OnlyEnforceIf(lit)
|
||||
|
||||
model.AddCircuit(arcs)
|
||||
|
||||
|
||||
@@ -21,23 +21,23 @@ def main():
|
||||
pywrapknapsack_solver.KnapsackSolver.
|
||||
KNAPSACK_MULTIDIMENSION_BRANCH_AND_BOUND_SOLVER, 'test')
|
||||
profits = [
|
||||
360, 83, 59, 130, 431, 67, 230, 52, 93, 125, 670, 892, 600, 38, 48,
|
||||
147, 78, 256, 63, 17, 120, 164, 432, 35, 92, 110, 22, 42, 50, 323, 514,
|
||||
28, 87, 73, 78, 15, 26, 78, 210, 36, 85, 189, 274, 43, 33, 10, 19, 389,
|
||||
276, 312
|
||||
360, 83, 59, 130, 431, 67, 230, 52, 93, 125, 670, 892, 600, 38, 48, 147,
|
||||
78, 256, 63, 17, 120, 164, 432, 35, 92, 110, 22, 42, 50, 323, 514, 28,
|
||||
87, 73, 78, 15, 26, 78, 210, 36, 85, 189, 274, 43, 33, 10, 19, 389, 276,
|
||||
312
|
||||
]
|
||||
weights = [[
|
||||
7, 0, 30, 22, 80, 94, 11, 81, 70, 64, 59, 18, 0, 36, 3, 8, 15, 42, 9,
|
||||
0, 42, 47, 52, 32, 26, 48, 55, 6, 29, 84, 2, 4, 18, 56, 7, 29, 93, 44,
|
||||
71, 3, 86, 66, 31, 65, 0, 79, 20, 65, 52, 13
|
||||
7, 0, 30, 22, 80, 94, 11, 81, 70, 64, 59, 18, 0, 36, 3, 8, 15, 42, 9, 0,
|
||||
42, 47, 52, 32, 26, 48, 55, 6, 29, 84, 2, 4, 18, 56, 7, 29, 93, 44, 71,
|
||||
3, 86, 66, 31, 65, 0, 79, 20, 65, 52, 13
|
||||
]]
|
||||
capacities = [850]
|
||||
optimal_profit = 7534
|
||||
solver.Init(profits, weights, capacities)
|
||||
computed_profit = solver.Solve()
|
||||
|
||||
print(('optimal profit = ' + str(computed_profit) + '/' +
|
||||
str(optimal_profit)))
|
||||
print((
|
||||
'optimal profit = ' + str(computed_profit) + '/' + str(optimal_profit)))
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
||||
@@ -46,8 +46,7 @@ def RunAssignmentOn4x4Matrix():
|
||||
print('No perfect matching exists.')
|
||||
elif solve_status == assignment.POSSIBLE_OVERFLOW:
|
||||
print(
|
||||
'Some input costs are too large and may cause an integer overflow.'
|
||||
)
|
||||
'Some input costs are too large and may cause an integer overflow.')
|
||||
|
||||
|
||||
def main():
|
||||
|
||||
@@ -70,12 +70,15 @@ def main():
|
||||
print('x: reduced cost =', x.reduced_cost())
|
||||
print('y: reduced cost =', y.reduced_cost())
|
||||
activities = solver.ComputeConstraintActivities()
|
||||
print('constraint0: dual value =', constraint0.dual_value(),
|
||||
' activities =', activities[constraint0.index()])
|
||||
print('constraint1: dual value =', constraint1.dual_value(),
|
||||
' activities =', activities[constraint1.index()])
|
||||
print('constraint2: dual value =', constraint2.dual_value(),
|
||||
' activities =', activities[constraint2.index()])
|
||||
print('constraint0: dual value =',
|
||||
constraint0.dual_value(), ' activities =',
|
||||
activities[constraint0.index()])
|
||||
print('constraint1: dual value =',
|
||||
constraint1.dual_value(), ' activities =',
|
||||
activities[constraint1.index()])
|
||||
print('constraint2: dual value =',
|
||||
constraint2.dual_value(), ' activities =',
|
||||
activities[constraint2.index()])
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
||||
@@ -53,8 +53,7 @@ def main(board_size):
|
||||
# Creates the variables.
|
||||
# The array index is the column, and the value is the row.
|
||||
queens = [
|
||||
model.NewIntVar(0, board_size - 1, 'x%i' % i)
|
||||
for i in range(board_size)
|
||||
model.NewIntVar(0, board_size - 1, 'x%i' % i) for i in range(board_size)
|
||||
]
|
||||
# Creates the constraints.
|
||||
|
||||
|
||||
@@ -63,8 +63,8 @@ def main():
|
||||
for n in all_nurses:
|
||||
for d in all_days:
|
||||
for s in all_shifts:
|
||||
shifts[(n, d,
|
||||
s)] = model.NewBoolVar('shift_n%id%is%i' % (n, d, s))
|
||||
shifts[(n, d, s)] = model.NewBoolVar('shift_n%id%is%i' % (n, d,
|
||||
s))
|
||||
|
||||
# Makes assignments different on each day, that is each shift is assigned at
|
||||
# most one nurse. As we have the same number of nurses and shifts, then each
|
||||
@@ -88,8 +88,8 @@ def main():
|
||||
works_shift = {}
|
||||
for n in all_nurses:
|
||||
for s in all_shifts:
|
||||
works_shift[(n,
|
||||
s)] = model.NewBoolVar('works_shift_n%is%i' % (n, s))
|
||||
works_shift[(n, s)] = model.NewBoolVar('works_shift_n%is%i' % (n,
|
||||
s))
|
||||
model.AddMaxEquality(works_shift[(n, s)],
|
||||
[shifts[(n, d, s)] for d in all_days])
|
||||
|
||||
|
||||
@@ -63,9 +63,10 @@ def MinCostFlow():
|
||||
print('Total flow', min_cost_flow.OptimalCost(), '/', expected_cost)
|
||||
for i in range(0, min_cost_flow.NumArcs()):
|
||||
if min_cost_flow.Flow(i) > 0:
|
||||
print('From source %d to target %d: cost %d' % (
|
||||
min_cost_flow.Tail(i), min_cost_flow.Head(i) - num_sources,
|
||||
min_cost_flow.UnitCost(i)))
|
||||
print('From source %d to target %d: cost %d' %
|
||||
(min_cost_flow.Tail(i),
|
||||
min_cost_flow.Head(i) - num_sources,
|
||||
min_cost_flow.UnitCost(i)))
|
||||
else:
|
||||
print('There was an issue with the min cost flow input.')
|
||||
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
@@ -80,8 +80,8 @@ class RandomMatrix(object):
|
||||
distance_max)
|
||||
|
||||
def Distance(self, manager, from_index, to_index):
|
||||
return self.matrix[manager.IndexToNode(from_index)][
|
||||
manager.IndexToNode(to_index)]
|
||||
return self.matrix[manager.IndexToNode(from_index)][manager.IndexToNode(
|
||||
to_index)]
|
||||
|
||||
|
||||
def main(args):
|
||||
|
||||
@@ -81,8 +81,7 @@ def solve_rcpsp(problem, proto_file, params):
|
||||
|
||||
horizon = problem.deadline if problem.deadline != -1 else problem.horizon
|
||||
if horizon == -1: # Naive computation.
|
||||
horizon = sum(
|
||||
max(r.duration for r in t.recipes) for t in problem.tasks)
|
||||
horizon = sum(max(r.duration for r in t.recipes) for t in problem.tasks)
|
||||
if problem.is_rcpsp_max:
|
||||
for t in problem.tasks:
|
||||
for sd in t.successor_delays:
|
||||
@@ -116,8 +115,7 @@ def solve_rcpsp(problem, proto_file, params):
|
||||
if len(task.recipes) == 1:
|
||||
# Create interval.
|
||||
recipe = task.recipes[0]
|
||||
task_starts[t] = model.NewIntVar(0, horizon,
|
||||
'start_of_task_%i' % t)
|
||||
task_starts[t] = model.NewIntVar(0, horizon, 'start_of_task_%i' % t)
|
||||
task_ends[t] = model.NewIntVar(0, horizon, 'end_of_task_%i' % t)
|
||||
interval = model.NewIntervalVar(task_starts[t], recipe.duration,
|
||||
task_ends[t], 'interval_%i' % t)
|
||||
@@ -173,8 +171,7 @@ def solve_rcpsp(problem, proto_file, params):
|
||||
presences_per_resource[res].append(is_present)
|
||||
|
||||
# Create the master interval for the task.
|
||||
task_starts[t] = model.NewIntVar(0, horizon,
|
||||
'start_of_task_%i' % t)
|
||||
task_starts[t] = model.NewIntVar(0, horizon, 'start_of_task_%i' % t)
|
||||
task_ends[t] = model.NewIntVar(0, horizon, 'end_of_task_%i' % t)
|
||||
duration = model.NewIntVar(min_size, max_size,
|
||||
'duration_of_task_%i' % t)
|
||||
@@ -187,8 +184,7 @@ def solve_rcpsp(problem, proto_file, params):
|
||||
model.Add(
|
||||
task_starts[t] == starts_per_task[t][r]).OnlyEnforceIf(p)
|
||||
model.Add(task_ends[t] == ends_per_task[t][r]).OnlyEnforceIf(p)
|
||||
model.Add(
|
||||
duration == task.recipes[r].duration).OnlyEnforceIf(p)
|
||||
model.Add(duration == task.recipes[r].duration).OnlyEnforceIf(p)
|
||||
model.Add(sum(presences_per_task[t]) == 1)
|
||||
|
||||
# Create makespan variable
|
||||
@@ -261,9 +257,8 @@ def solve_rcpsp(problem, proto_file, params):
|
||||
# Objective.
|
||||
if problem.is_resource_investment:
|
||||
objective = model.NewIntVar(0, max_cost, 'capacity_costs')
|
||||
model.Add(objective == sum(
|
||||
problem.resources[i].unit_cost * capacities[i]
|
||||
for i in range(len(capacities))))
|
||||
model.Add(objective == sum(problem.resources[i].unit_cost * capacities[
|
||||
i] for i in range(len(capacities))))
|
||||
else:
|
||||
objective = makespan
|
||||
|
||||
|
||||
@@ -308,8 +308,8 @@ def solve_shift_scheduling(params, output_proto):
|
||||
works = [work[e, shift, d] for d in range(num_days)]
|
||||
variables, coeffs = add_soft_sequence_constraint(
|
||||
model, works, hard_min, soft_min, min_cost, soft_max, hard_max,
|
||||
max_cost,
|
||||
'shift_constraint(employee %i, shift %i)' % (e, shift))
|
||||
max_cost, 'shift_constraint(employee %i, shift %i)' % (e,
|
||||
shift))
|
||||
obj_bool_vars.extend(variables)
|
||||
obj_bool_coeffs.extend(coeffs)
|
||||
|
||||
@@ -367,9 +367,9 @@ def solve_shift_scheduling(params, output_proto):
|
||||
# Objective
|
||||
model.Minimize(
|
||||
sum(obj_bool_vars[i] * obj_bool_coeffs[i]
|
||||
for i in range(len(obj_bool_vars))) + sum(
|
||||
obj_int_vars[i] * obj_int_coeffs[i]
|
||||
for i in range(len(obj_int_vars))))
|
||||
for i in range(len(obj_bool_vars)))
|
||||
+ sum(obj_int_vars[i] * obj_int_coeffs[i]
|
||||
for i in range(len(obj_int_vars))))
|
||||
|
||||
if output_proto:
|
||||
print('Writing proto to %s' % output_proto)
|
||||
|
||||
@@ -198,8 +198,7 @@ def main(args):
|
||||
name_suffix = '_%i' % job_id
|
||||
start = model.NewIntVar(release_date, due_date, 's' + name_suffix)
|
||||
end = model.NewIntVar(release_date, due_date, 'e' + name_suffix)
|
||||
interval = model.NewIntervalVar(start, duration, end,
|
||||
'i' + name_suffix)
|
||||
interval = model.NewIntervalVar(start, duration, end, 'i' + name_suffix)
|
||||
starts.append(start)
|
||||
ends.append(end)
|
||||
intervals.append(interval)
|
||||
@@ -235,8 +234,8 @@ def main(args):
|
||||
model.Add(starts[j] == ends[i] +
|
||||
setup_times[i + 1][j]).OnlyEnforceIf(lit)
|
||||
else:
|
||||
model.Add(starts[j] >= ends[i] +
|
||||
setup_times[i + 1][j]).OnlyEnforceIf(lit)
|
||||
model.Add(starts[j] >=
|
||||
ends[i] + setup_times[i + 1][j]).OnlyEnforceIf(lit)
|
||||
|
||||
model.AddCircuit(arcs)
|
||||
|
||||
@@ -269,9 +268,9 @@ def main(args):
|
||||
solver.SolveWithSolutionCallback(model, solution_printer)
|
||||
print(solver.ResponseStats())
|
||||
for job_id in all_jobs:
|
||||
print(
|
||||
'job %i starts at %i end ends at %i' %
|
||||
(job_id, solver.Value(starts[job_id]), solver.Value(ends[job_id])))
|
||||
print('job %i starts at %i end ends at %i' %
|
||||
(job_id, solver.Value(starts[job_id]),
|
||||
solver.Value(ends[job_id])))
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
||||
@@ -347,14 +347,12 @@ def steel_mill_slab(problem, break_symmetries, output_proto):
|
||||
for s in all_slabs
|
||||
]
|
||||
color_is_in_slab = [[
|
||||
model.NewBoolVar('color_%i_in_slab_%i' % (c + 1, s))
|
||||
for c in all_colors
|
||||
model.NewBoolVar('color_%i_in_slab_%i' % (c + 1, s)) for c in all_colors
|
||||
] for s in all_slabs]
|
||||
|
||||
# Compute load of all slabs.
|
||||
for s in all_slabs:
|
||||
model.Add(
|
||||
sum(assign[o][s] * widths[o] for o in all_orders) == loads[s])
|
||||
model.Add(sum(assign[o][s] * widths[o] for o in all_orders) == loads[s])
|
||||
|
||||
# Orders are assigned to one slab.
|
||||
for o in all_orders:
|
||||
@@ -444,8 +442,9 @@ def steel_mill_slab(problem, break_symmetries, output_proto):
|
||||
|
||||
### Output the solution.
|
||||
if status == cp_model.OPTIMAL:
|
||||
print('Loss = %i, time = %f s, %i conflicts' % (
|
||||
solver.ObjectiveValue(), solver.WallTime(), solver.NumConflicts()))
|
||||
print('Loss = %i, time = %f s, %i conflicts' %
|
||||
(solver.ObjectiveValue(), solver.WallTime(),
|
||||
solver.NumConflicts()))
|
||||
else:
|
||||
print('No solution')
|
||||
|
||||
@@ -477,9 +476,7 @@ def collect_valid_slabs(capacities, colors, widths, loss_array, all_colors):
|
||||
assign = [model.NewBoolVar('assign_%i' % o) for o in all_orders]
|
||||
load = model.NewIntVar(0, max_capacity, 'load')
|
||||
|
||||
color_in_slab = [
|
||||
model.NewBoolVar('color_%i' % (c + 1)) for c in all_colors
|
||||
]
|
||||
color_in_slab = [model.NewBoolVar('color_%i' % (c + 1)) for c in all_colors]
|
||||
|
||||
# Compute load.
|
||||
model.Add(sum(assign[o] * widths[o] for o in all_orders) == load)
|
||||
@@ -538,9 +535,7 @@ def steel_mill_slab_with_valid_slabs(problem, break_symmetries, output_proto):
|
||||
assign = [[
|
||||
model.NewBoolVar('assign_%i_to_slab_%i' % (o, s)) for s in all_slabs
|
||||
] for o in all_orders]
|
||||
loads = [
|
||||
model.NewIntVar(0, max_capacity, 'load_%i' % s) for s in all_slabs
|
||||
]
|
||||
loads = [model.NewIntVar(0, max_capacity, 'load_%i' % s) for s in all_slabs]
|
||||
losses = [model.NewIntVar(0, max_loss, 'loss_%i' % s) for s in all_slabs]
|
||||
|
||||
unsorted_valid_slabs = collect_valid_slabs(capacities, colors, widths,
|
||||
@@ -553,8 +548,8 @@ def steel_mill_slab_with_valid_slabs(problem, break_symmetries, output_proto):
|
||||
|
||||
for s in all_slabs:
|
||||
model.AddAllowedAssignments(
|
||||
[assign[o][s] for o in all_orders] + [losses[s], loads[s]],
|
||||
valid_slabs)
|
||||
[assign[o][s]
|
||||
for o in all_orders] + [losses[s], loads[s]], valid_slabs)
|
||||
|
||||
# Orders are assigned to one slab.
|
||||
for o in all_orders:
|
||||
@@ -595,8 +590,7 @@ def steel_mill_slab_with_valid_slabs(problem, break_symmetries, output_proto):
|
||||
for w, os in local_width_to_order.items():
|
||||
if len(os) > 1:
|
||||
for p in range(len(os) - 1):
|
||||
ordered_equivalent_orders.append((os[p],
|
||||
os[p + 1]))
|
||||
ordered_equivalent_orders.append((os[p], os[p + 1]))
|
||||
for w, os in width_to_unique_color_order.items():
|
||||
if len(os) > 1:
|
||||
for p in range(len(os) - 1):
|
||||
@@ -640,8 +634,9 @@ def steel_mill_slab_with_valid_slabs(problem, break_symmetries, output_proto):
|
||||
|
||||
### Output the solution.
|
||||
if status == cp_model.OPTIMAL:
|
||||
print('Loss = %i, time = %f s, %i conflicts' % (
|
||||
solver.ObjectiveValue(), solver.WallTime(), solver.NumConflicts()))
|
||||
print('Loss = %i, time = %f s, %i conflicts' %
|
||||
(solver.ObjectiveValue(), solver.WallTime(),
|
||||
solver.NumConflicts()))
|
||||
else:
|
||||
print('No solution')
|
||||
|
||||
@@ -685,8 +680,7 @@ def steel_mill_slab_with_column_generation(problem, output_proto):
|
||||
|
||||
for o in all_orders:
|
||||
model.Add(
|
||||
sum(selected[i] for i in all_valid_slabs
|
||||
if valid_slabs[i][o]) == 1)
|
||||
sum(selected[i] for i in all_valid_slabs if valid_slabs[i][o]) == 1)
|
||||
|
||||
# Redundant constraint (sum of loads == sum of widths).
|
||||
model.Add(
|
||||
@@ -696,8 +690,8 @@ def steel_mill_slab_with_column_generation(problem, output_proto):
|
||||
# Objective.
|
||||
max_loss = max(valid_slabs[i][-2] for i in all_valid_slabs)
|
||||
obj = model.NewIntVar(0, num_slabs * max_loss, 'obj')
|
||||
model.Add(obj == sum(
|
||||
selected[i] * valid_slabs[i][-2] for i in all_valid_slabs))
|
||||
model.Add(
|
||||
obj == sum(selected[i] * valid_slabs[i][-2] for i in all_valid_slabs))
|
||||
model.Minimize(obj)
|
||||
|
||||
print('Model created')
|
||||
@@ -715,8 +709,9 @@ def steel_mill_slab_with_column_generation(problem, output_proto):
|
||||
|
||||
### Output the solution.
|
||||
if status == cp_model.OPTIMAL:
|
||||
print('Loss = %i, time = %f s, %i conflicts' % (
|
||||
solver.ObjectiveValue(), solver.WallTime(), solver.NumConflicts()))
|
||||
print('Loss = %i, time = %f s, %i conflicts' %
|
||||
(solver.ObjectiveValue(), solver.WallTime(),
|
||||
solver.NumConflicts()))
|
||||
else:
|
||||
print('No solution')
|
||||
|
||||
|
||||
@@ -22,203 +22,156 @@ from ortools.linear_solver import pywraplp
|
||||
def main():
|
||||
"""Entry point of the program"""
|
||||
# Nutrient minimums.
|
||||
nutrients = [['Calories (kcal)', 3], ['Protein (g)', 70],
|
||||
['Calcium (g)', 0.8], ['Iron (mg)', 12],
|
||||
['Vitamin A (KIU)', 5], ['Vitamin B1 (mg)', 1.8],
|
||||
['Vitamin B2 (mg)', 2.7], ['Niacin (mg)', 18],
|
||||
['Vitamin C (mg)', 75]]
|
||||
nutrients = [['Calories (kcal)', 3], ['Protein (g)', 70], [
|
||||
'Calcium (g)', 0.8
|
||||
], ['Iron (mg)', 12], ['Vitamin A (KIU)', 5], ['Vitamin B1 (mg)', 1.8],
|
||||
['Vitamin B2 (mg)', 2.7], ['Niacin (mg)',
|
||||
18], ['Vitamin C (mg)', 75]]
|
||||
|
||||
# Commodity, Unit, 1939 price (cents), Calories (kcal), Protein (g), Calcium (g), Iron (mg),
|
||||
# Vitamin A (KIU), Vitamin B1 (mg), Vitamin B2 (mg), Niacin (mg), Vitamin C (mg)
|
||||
data = [
|
||||
[
|
||||
'Wheat Flour (Enriched)', '10 lb.', 36, 44.7, 1411, 2, 365, 0,
|
||||
55.4, 33.3, 441, 0
|
||||
], ['Macaroni', '1 lb.', 14.1, 11.6, 418, 0.7, 54, 0, 3.2, 1.9, 68, 0],
|
||||
[
|
||||
'Wheat Cereal (Enriched)', '28 oz.', 24.2, 11.8, 377, 14.4, 175, 0,
|
||||
14.4, 8.8, 114, 0
|
||||
],
|
||||
['Corn Flakes', '8 oz.', 7.1, 11.4, 252, 0.1, 56, 0, 13.5, 2.3, 68, 0],
|
||||
[
|
||||
'Corn Meal', '1 lb.', 4.6, 36.0, 897, 1.7, 99, 30.9, 17.4, 7.9,
|
||||
106, 0
|
||||
],
|
||||
[
|
||||
'Hominy Grits', '24 oz.', 8.5, 28.6, 680, 0.8, 80, 0, 10.6, 1.6,
|
||||
110, 0
|
||||
], ['Rice', '1 lb.', 7.5, 21.2, 460, 0.6, 41, 0, 2, 4.8, 60, 0],
|
||||
[
|
||||
'Rolled Oats', '1 lb.', 7.1, 25.3, 907, 5.1, 341, 0, 37.1, 8.9, 64,
|
||||
0
|
||||
],
|
||||
[
|
||||
'White Bread (Enriched)', '1 lb.', 7.9, 15.0, 488, 2.5, 115, 0,
|
||||
13.8, 8.5, 126, 0
|
||||
],
|
||||
[
|
||||
'Whole Wheat Bread', '1 lb.', 9.1, 12.2, 484, 2.7, 125, 0, 13.9,
|
||||
6.4, 160, 0
|
||||
], ['Rye Bread', '1 lb.', 9.1, 12.4, 439, 1.1, 82, 0, 9.9, 3, 66, 0],
|
||||
['Pound Cake', '1 lb.', 24.8, 8.0, 130, 0.4, 31, 18.9, 2.8, 3, 17, 0],
|
||||
['Soda Crackers', '1 lb.', 15.1, 12.5, 288, 0.5, 50, 0, 0, 0, 0, 0],
|
||||
['Milk', '1 qt.', 11, 6.1, 310, 10.5, 18, 16.8, 4, 16, 7, 177],
|
||||
[
|
||||
'Evaporated Milk (can)', '14.5 oz.', 6.7, 8.4, 422, 15.1, 9, 26, 3,
|
||||
23.5, 11, 60
|
||||
], ['Butter', '1 lb.', 30.8, 10.8, 9, 0.2, 3, 44.2, 0, 0.2, 2, 0],
|
||||
['Oleomargarine', '1 lb.', 16.1, 20.6, 17, 0.6, 6, 55.8, 0.2, 0, 0, 0],
|
||||
['Eggs', '1 doz.', 32.6, 2.9, 238, 1.0, 52, 18.6, 2.8, 6.5, 1, 0],
|
||||
[
|
||||
'Cheese (Cheddar)', '1 lb.', 24.2, 7.4, 448, 16.4, 19, 28.1, 0.8,
|
||||
10.3, 4, 0
|
||||
], ['Cream', '1/2 pt.', 14.1, 3.5, 49, 1.7, 3, 16.9, 0.6, 2.5, 0, 17],
|
||||
[
|
||||
'Peanut Butter', '1 lb.', 17.9, 15.7, 661, 1.0, 48, 0, 9.6, 8.1,
|
||||
471, 0
|
||||
],
|
||||
['Mayonnaise', '1/2 pt.', 16.7, 8.6, 18, 0.2, 8, 2.7, 0.4, 0.5, 0, 0],
|
||||
['Crisco', '1 lb.', 20.3, 20.1, 0, 0, 0, 0, 0, 0, 0, 0],
|
||||
['Lard', '1 lb.', 9.8, 41.7, 0, 0, 0, 0.2, 0, 0.5, 5, 0],
|
||||
[
|
||||
'Sirloin Steak', '1 lb.', 39.6, 2.9, 166, 0.1, 34, 0.2, 2.1, 2.9,
|
||||
69, 0
|
||||
],
|
||||
[
|
||||
'Round Steak', '1 lb.', 36.4, 2.2, 214, 0.1, 32, 0.4, 2.5, 2.4, 87,
|
||||
0
|
||||
], ['Rib Roast', '1 lb.', 29.2, 3.4, 213, 0.1, 33, 0, 0, 2, 0, 0],
|
||||
['Chuck Roast', '1 lb.', 22.6, 3.6, 309, 0.2, 46, 0.4, 1, 4, 120, 0],
|
||||
['Plate', '1 lb.', 14.6, 8.5, 404, 0.2, 62, 0, 0.9, 0, 0, 0],
|
||||
[
|
||||
'Liver (Beef)', '1 lb.', 26.8, 2.2, 333, 0.2, 139, 169.2, 6.4,
|
||||
50.8, 316, 525
|
||||
],
|
||||
['Leg of Lamb', '1 lb.', 27.6, 3.1, 245, 0.1, 20, 0, 2.8, 3.9, 86, 0],
|
||||
[
|
||||
'Lamb Chops (Rib)', '1 lb.', 36.6, 3.3, 140, 0.1, 15, 0, 1.7, 2.7,
|
||||
54, 0
|
||||
], [
|
||||
'Pork Chops', '1 lb.', 30.7, 3.5, 196, 0.2, 30, 0, 17.4, 2.7, 60, 0
|
||||
],
|
||||
[
|
||||
'Pork Loin Roast', '1 lb.', 24.2, 4.4, 249, 0.3, 37, 0, 18.2, 3.6,
|
||||
79, 0
|
||||
], ['Bacon', '1 lb.', 25.6, 10.4, 152, 0.2, 23, 0, 1.8, 1.8, 71, 0],
|
||||
['Ham, smoked', '1 lb.', 27.4, 6.7, 212, 0.2, 31, 0, 9.9, 3.3, 50, 0],
|
||||
['Salt Pork', '1 lb.', 16, 18.8, 164, 0.1, 26, 0, 1.4, 1.8, 0, 0],
|
||||
[
|
||||
'Roasting Chicken', '1 lb.', 30.3, 1.8, 184, 0.1, 30, 0.1, 0.9,
|
||||
1.8, 68, 46
|
||||
],
|
||||
['Veal Cutlets', '1 lb.', 42.3, 1.7, 156, 0.1, 24, 0, 1.4, 2.4, 57, 0],
|
||||
[
|
||||
'Salmon, Pink (can)', '16 oz.', 13, 5.8, 705, 6.8, 45, 3.5, 1, 4.9,
|
||||
209, 0
|
||||
], ['Apples', '1 lb.', 4.4, 5.8, 27, 0.5, 36, 7.3, 3.6, 2.7, 5, 544],
|
||||
['Bananas', '1 lb.', 6.1, 4.9, 60, 0.4, 30, 17.4, 2.5, 3.5, 28, 498],
|
||||
['Lemons', '1 doz.', 26, 1.0, 21, 0.5, 14, 0, 0.5, 0, 4, 952],
|
||||
[
|
||||
'Oranges', '1 doz.', 30.9, 2.2, 40, 1.1, 18, 11.1, 3.6, 1.3, 10,
|
||||
1998
|
||||
],
|
||||
[
|
||||
'Green Beans', '1 lb.', 7.1, 2.4, 138, 3.7, 80, 69, 4.3, 5.8, 37,
|
||||
862
|
||||
], ['Cabbage', '1 lb.', 3.7, 2.6, 125, 4.0, 36, 7.2, 9, 4.5, 26, 5369],
|
||||
[
|
||||
'Carrots', '1 bunch', 4.7, 2.7, 73, 2.8, 43, 188.5, 6.1, 4.3, 89,
|
||||
608
|
||||
], ['Celery', '1 stalk', 7.3, 0.9, 51, 3.0, 23, 0.9, 1.4, 1.4, 9, 313],
|
||||
['Lettuce', '1 head', 8.2, 0.4, 27, 1.1, 22, 112.4, 1.8, 3.4, 11, 449],
|
||||
['Onions', '1 lb.', 3.6, 5.8, 166, 3.8, 59, 16.6, 4.7, 5.9, 21, 1184],
|
||||
[
|
||||
'Potatoes', '15 lb.', 34, 14.3, 336, 1.8, 118, 6.7, 29.4, 7.1, 198,
|
||||
2522
|
||||
],
|
||||
[
|
||||
'Spinach', '1 lb.', 8.1, 1.1, 106, 0, 138, 918.4, 5.7, 13.8, 33,
|
||||
2755
|
||||
],
|
||||
[
|
||||
'Sweet Potatoes', '1 lb.', 5.1, 9.6, 138, 2.7, 54, 290.7, 8.4, 5.4,
|
||||
83, 1912
|
||||
],
|
||||
[
|
||||
'Peaches (can)', 'No. 2 1/2', 16.8, 3.7, 20, 0.4, 10, 21.5, 0.5, 1,
|
||||
31, 196
|
||||
],
|
||||
[
|
||||
'Pears (can)', 'No. 2 1/2', 20.4, 3.0, 8, 0.3, 8, 0.8, 0.8, 0.8, 5,
|
||||
81
|
||||
],
|
||||
[
|
||||
'Pineapple (can)', 'No. 2 1/2', 21.3, 2.4, 16, 0.4, 8, 2, 2.8, 0.8,
|
||||
7, 399
|
||||
],
|
||||
[
|
||||
'Asparagus (can)', 'No. 2', 27.7, 0.4, 33, 0.3, 12, 16.3, 1.4, 2.1,
|
||||
17, 272
|
||||
],
|
||||
[
|
||||
'Green Beans (can)', 'No. 2', 10, 1.0, 54, 2, 65, 53.9, 1.6, 4.3,
|
||||
32, 431
|
||||
],
|
||||
[
|
||||
'Pork and Beans (can)', '16 oz.', 7.1, 7.5, 364, 4, 134, 3.5, 8.3,
|
||||
7.7, 56, 0
|
||||
],
|
||||
[
|
||||
'Corn (can)', 'No. 2', 10.4, 5.2, 136, 0.2, 16, 12, 1.6, 2.7, 42,
|
||||
218
|
||||
],
|
||||
[
|
||||
'Peas (can)', 'No. 2', 13.8, 2.3, 136, 0.6, 45, 34.9, 4.9, 2.5, 37,
|
||||
370
|
||||
],
|
||||
[
|
||||
'Tomatoes (can)', 'No. 2', 8.6, 1.3, 63, 0.7, 38, 53.2, 3.4, 2.5,
|
||||
36, 1253
|
||||
],
|
||||
[
|
||||
'Tomato Soup (can)', '10 1/2 oz.', 7.6, 1.6, 71, 0.6, 43, 57.9,
|
||||
3.5, 2.4, 67, 862
|
||||
],
|
||||
[
|
||||
'Peaches, Dried', '1 lb.', 15.7, 8.5, 87, 1.7, 173, 86.8, 1.2, 4.3,
|
||||
55, 57
|
||||
],
|
||||
[
|
||||
'Prunes, Dried', '1 lb.', 9, 12.8, 99, 2.5, 154, 85.7, 3.9, 4.3,
|
||||
65, 257
|
||||
],
|
||||
[
|
||||
'Raisins, Dried', '15 oz.', 9.4, 13.5, 104, 2.5, 136, 4.5, 6.3,
|
||||
1.4, 24, 136
|
||||
],
|
||||
[
|
||||
'Peas, Dried', '1 lb.', 7.9, 20.0, 1367, 4.2, 345, 2.9, 28.7, 18.4,
|
||||
162, 0
|
||||
],
|
||||
[
|
||||
'Lima Beans, Dried', '1 lb.', 8.9, 17.4, 1055, 3.7, 459, 5.1, 26.9,
|
||||
38.2, 93, 0
|
||||
],
|
||||
[
|
||||
'Navy Beans, Dried', '1 lb.', 5.9, 26.9, 1691, 11.4, 792, 0, 38.4,
|
||||
24.6, 217, 0
|
||||
], ['Coffee', '1 lb.', 22.4, 0, 0, 0, 0, 0, 4, 5.1, 50, 0],
|
||||
['Tea', '1/4 lb.', 17.4, 0, 0, 0, 0, 0, 0, 2.3, 42, 0],
|
||||
['Cocoa', '8 oz.', 8.6, 8.7, 237, 3, 72, 0, 2, 11.9, 40, 0],
|
||||
['Chocolate', '8 oz.', 16.2, 8.0, 77, 1.3, 39, 0, 0.9, 3.4, 14, 0],
|
||||
['Sugar', '10 lb.', 51.7, 34.9, 0, 0, 0, 0, 0, 0, 0, 0],
|
||||
['Corn Syrup', '24 oz.', 13.7, 14.7, 0, 0.5, 74, 0, 0, 0, 5, 0],
|
||||
['Molasses', '18 oz.', 13.6, 9.0, 0, 10.3, 244, 0, 1.9, 7.5, 146, 0],
|
||||
[
|
||||
'Strawberry Preserves', '1 lb.', 20.5, 6.4, 11, 0.4, 7, 0.2, 0.2,
|
||||
0.4, 3, 0
|
||||
]
|
||||
]
|
||||
data = [[
|
||||
'Wheat Flour (Enriched)', '10 lb.', 36, 44.7, 1411, 2, 365, 0, 55.4,
|
||||
33.3, 441, 0
|
||||
], ['Macaroni', '1 lb.', 14.1, 11.6, 418, 0.7, 54, 0, 3.2, 1.9, 68, 0], [
|
||||
'Wheat Cereal (Enriched)', '28 oz.', 24.2, 11.8, 377, 14.4, 175, 0,
|
||||
14.4, 8.8, 114, 0
|
||||
], ['Corn Flakes', '8 oz.', 7.1, 11.4, 252, 0.1, 56, 0, 13.5, 2.3, 68, 0], [
|
||||
'Corn Meal', '1 lb.', 4.6, 36.0, 897, 1.7, 99, 30.9, 17.4, 7.9, 106, 0
|
||||
], [
|
||||
'Hominy Grits', '24 oz.', 8.5, 28.6, 680, 0.8, 80, 0, 10.6, 1.6, 110, 0
|
||||
], ['Rice', '1 lb.', 7.5, 21.2, 460, 0.6, 41, 0, 2, 4.8, 60, 0], [
|
||||
'Rolled Oats', '1 lb.', 7.1, 25.3, 907, 5.1, 341, 0, 37.1, 8.9, 64, 0
|
||||
], [
|
||||
'White Bread (Enriched)', '1 lb.', 7.9, 15.0, 488, 2.5, 115, 0, 13.8,
|
||||
8.5, 126, 0
|
||||
], [
|
||||
'Whole Wheat Bread', '1 lb.', 9.1, 12.2, 484, 2.7, 125, 0, 13.9, 6.4,
|
||||
160, 0
|
||||
], ['Rye Bread', '1 lb.', 9.1, 12.4, 439, 1.1, 82, 0, 9.9, 3, 66, 0], [
|
||||
'Pound Cake', '1 lb.', 24.8, 8.0, 130, 0.4, 31, 18.9, 2.8, 3, 17, 0
|
||||
], ['Soda Crackers', '1 lb.', 15.1, 12.5, 288, 0.5, 50, 0, 0, 0, 0, 0], [
|
||||
'Milk', '1 qt.', 11, 6.1, 310, 10.5, 18, 16.8, 4, 16, 7, 177
|
||||
], [
|
||||
'Evaporated Milk (can)', '14.5 oz.', 6.7, 8.4, 422, 15.1, 9, 26, 3,
|
||||
23.5, 11, 60
|
||||
], ['Butter', '1 lb.', 30.8, 10.8, 9, 0.2, 3, 44.2, 0, 0.2, 2, 0], [
|
||||
'Oleomargarine', '1 lb.', 16.1, 20.6, 17, 0.6, 6, 55.8, 0.2, 0, 0, 0
|
||||
], ['Eggs', '1 doz.', 32.6, 2.9, 238, 1.0, 52, 18.6, 2.8, 6.5, 1, 0], [
|
||||
'Cheese (Cheddar)', '1 lb.', 24.2, 7.4, 448, 16.4, 19, 28.1, 0.8, 10.3,
|
||||
4, 0
|
||||
], ['Cream', '1/2 pt.', 14.1, 3.5, 49, 1.7, 3, 16.9, 0.6, 2.5, 0, 17], [
|
||||
'Peanut Butter', '1 lb.', 17.9, 15.7, 661, 1.0, 48, 0, 9.6, 8.1, 471, 0
|
||||
], ['Mayonnaise', '1/2 pt.', 16.7, 8.6, 18, 0.2, 8, 2.7, 0.4, 0.5, 0, 0], [
|
||||
'Crisco', '1 lb.', 20.3, 20.1, 0, 0, 0, 0, 0, 0, 0, 0
|
||||
], ['Lard', '1 lb.', 9.8, 41.7, 0, 0, 0, 0.2, 0, 0.5, 5, 0], [
|
||||
'Sirloin Steak', '1 lb.', 39.6, 2.9, 166, 0.1, 34, 0.2, 2.1, 2.9, 69, 0
|
||||
], ['Round Steak', '1 lb.', 36.4, 2.2, 214, 0.1, 32, 0.4, 2.5, 2.4, 87, 0
|
||||
], ['Rib Roast', '1 lb.', 29.2, 3.4, 213, 0.1, 33, 0, 0, 2, 0, 0], [
|
||||
'Chuck Roast', '1 lb.', 22.6, 3.6, 309, 0.2, 46, 0.4, 1, 4, 120, 0
|
||||
], ['Plate', '1 lb.', 14.6, 8.5, 404, 0.2, 62, 0, 0.9, 0, 0, 0], [
|
||||
'Liver (Beef)', '1 lb.', 26.8, 2.2, 333, 0.2, 139, 169.2, 6.4, 50.8,
|
||||
316, 525
|
||||
], [
|
||||
'Leg of Lamb', '1 lb.', 27.6, 3.1, 245, 0.1, 20, 0, 2.8, 3.9, 86, 0
|
||||
], [
|
||||
'Lamb Chops (Rib)',
|
||||
'1 lb.', 36.6, 3.3, 140, 0.1, 15, 0, 1.7, 2.7, 54, 0
|
||||
], [
|
||||
'Pork Chops', '1 lb.', 30.7, 3.5, 196, 0.2, 30, 0, 17.4, 2.7, 60, 0
|
||||
], [
|
||||
'Pork Loin Roast',
|
||||
'1 lb.', 24.2, 4.4, 249, 0.3, 37, 0, 18.2, 3.6, 79, 0
|
||||
], ['Bacon', '1 lb.', 25.6, 10.4, 152, 0.2, 23, 0, 1.8, 1.8, 71, 0], [
|
||||
'Ham, smoked', '1 lb.', 27.4, 6.7, 212, 0.2, 31, 0, 9.9, 3.3, 50, 0
|
||||
], ['Salt Pork', '1 lb.', 16, 18.8, 164, 0.1, 26, 0, 1.4, 1.8, 0, 0], [
|
||||
'Roasting Chicken', '1 lb.', 30.3, 1.8, 184, 0.1, 30, 0.1, 0.9, 1.8,
|
||||
68, 46
|
||||
], [
|
||||
'Veal Cutlets', '1 lb.', 42.3, 1.7, 156, 0.1, 24, 0, 1.4, 2.4, 57, 0
|
||||
], [
|
||||
'Salmon, Pink (can)', '16 oz.', 13, 5.8, 705, 6.8, 45, 3.5,
|
||||
1, 4.9, 209, 0
|
||||
], ['Apples', '1 lb.', 4.4, 5.8, 27, 0.5, 36, 7.3, 3.6, 2.7, 5, 544], [
|
||||
'Bananas', '1 lb.', 6.1, 4.9, 60, 0.4, 30, 17.4, 2.5, 3.5, 28, 498
|
||||
], ['Lemons', '1 doz.', 26, 1.0, 21, 0.5, 14, 0, 0.5, 0, 4, 952], [
|
||||
'Oranges', '1 doz.', 30.9, 2.2, 40, 1.1, 18, 11.1, 3.6, 1.3, 10, 1998
|
||||
], [
|
||||
'Green Beans', '1 lb.', 7.1, 2.4, 138, 3.7, 80, 69, 4.3, 5.8, 37, 862
|
||||
], ['Cabbage', '1 lb.', 3.7, 2.6, 125, 4.0, 36, 7.2, 9, 4.5, 26, 5369], [
|
||||
'Carrots', '1 bunch', 4.7, 2.7, 73, 2.8, 43, 188.5, 6.1, 4.3, 89, 608
|
||||
], ['Celery', '1 stalk', 7.3, 0.9, 51, 3.0, 23, 0.9, 1.4, 1.4, 9, 313], [
|
||||
'Lettuce', '1 head', 8.2, 0.4, 27, 1.1, 22, 112.4, 1.8, 3.4, 11, 449
|
||||
], ['Onions', '1 lb.', 3.6, 5.8, 166, 3.8, 59, 16.6, 4.7, 5.9, 21,
|
||||
1184], [
|
||||
'Potatoes', '15 lb.', 34, 14.3, 336, 1.8, 118, 6.7, 29.4, 7.1,
|
||||
198, 2522
|
||||
], [
|
||||
'Spinach', '1 lb.', 8.1, 1.1, 106, 0, 138, 918.4, 5.7, 13.8, 33,
|
||||
2755
|
||||
], [
|
||||
'Sweet Potatoes', '1 lb.', 5.1, 9.6, 138, 2.7, 54, 290.7, 8.4,
|
||||
5.4, 83, 1912
|
||||
], [
|
||||
'Peaches (can)', 'No. 2 1/2', 16.8, 3.7, 20, 0.4, 10, 21.5, 0.5,
|
||||
1, 31, 196
|
||||
], [
|
||||
'Pears (can)', 'No. 2 1/2', 20.4, 3.0, 8, 0.3, 8, 0.8, 0.8, 0.8,
|
||||
5, 81
|
||||
], [
|
||||
'Pineapple (can)', 'No. 2 1/2', 21.3, 2.4, 16, 0.4, 8, 2, 2.8,
|
||||
0.8, 7, 399
|
||||
], [
|
||||
'Asparagus (can)', 'No. 2', 27.7, 0.4, 33, 0.3, 12, 16.3, 1.4,
|
||||
2.1, 17, 272
|
||||
], [
|
||||
'Green Beans (can)', 'No. 2', 10, 1.0, 54, 2, 65, 53.9, 1.6, 4.3,
|
||||
32, 431
|
||||
], [
|
||||
'Pork and Beans (can)', '16 oz.', 7.1, 7.5, 364, 4, 134, 3.5,
|
||||
8.3, 7.7, 56, 0
|
||||
], [
|
||||
'Corn (can)', 'No. 2', 10.4, 5.2, 136, 0.2, 16, 12, 1.6, 2.7, 42,
|
||||
218
|
||||
], [
|
||||
'Peas (can)', 'No. 2', 13.8, 2.3, 136, 0.6, 45, 34.9, 4.9, 2.5,
|
||||
37, 370
|
||||
], [
|
||||
'Tomatoes (can)', 'No. 2', 8.6, 1.3, 63, 0.7, 38, 53.2, 3.4, 2.5,
|
||||
36, 1253
|
||||
], [
|
||||
'Tomato Soup (can)', '10 1/2 oz.', 7.6, 1.6, 71, 0.6, 43, 57.9,
|
||||
3.5, 2.4, 67, 862
|
||||
], [
|
||||
'Peaches, Dried', '1 lb.', 15.7, 8.5, 87, 1.7, 173, 86.8, 1.2,
|
||||
4.3, 55, 57
|
||||
], [
|
||||
'Prunes, Dried', '1 lb.', 9, 12.8, 99, 2.5, 154, 85.7, 3.9, 4.3,
|
||||
65, 257
|
||||
], [
|
||||
'Raisins, Dried', '15 oz.', 9.4, 13.5, 104, 2.5, 136, 4.5, 6.3,
|
||||
1.4, 24, 136
|
||||
], [
|
||||
'Peas, Dried', '1 lb.', 7.9, 20.0, 1367, 4.2, 345, 2.9, 28.7,
|
||||
18.4, 162, 0
|
||||
], [
|
||||
'Lima Beans, Dried', '1 lb.', 8.9, 17.4, 1055, 3.7, 459, 5.1,
|
||||
26.9, 38.2, 93, 0
|
||||
], [
|
||||
'Navy Beans, Dried', '1 lb.', 5.9, 26.9, 1691, 11.4, 792, 0,
|
||||
38.4, 24.6, 217, 0
|
||||
], ['Coffee', '1 lb.', 22.4, 0, 0, 0, 0, 0, 4, 5.1, 50,
|
||||
0], ['Tea', '1/4 lb.', 17.4, 0, 0, 0, 0, 0, 0, 2.3, 42, 0],
|
||||
['Cocoa', '8 oz.', 8.6, 8.7, 237, 3, 72, 0, 2, 11.9, 40, 0], [
|
||||
'Chocolate', '8 oz.', 16.2, 8.0, 77, 1.3, 39, 0, 0.9, 3.4, 14, 0
|
||||
], ['Sugar', '10 lb.', 51.7, 34.9, 0, 0, 0, 0, 0, 0, 0, 0],
|
||||
['Corn Syrup', '24 oz.', 13.7, 14.7, 0, 0.5, 74, 0, 0, 0, 5, 0], [
|
||||
'Molasses', '18 oz.', 13.6, 9.0, 0, 10.3, 244, 0, 1.9, 7.5, 146,
|
||||
0
|
||||
], [
|
||||
'Strawberry Preserves', '1 lb.', 20.5, 6.4, 11, 0.4, 7, 0.2,
|
||||
0.2, 0.4, 3, 0
|
||||
]]
|
||||
|
||||
# Instantiate a Glop solver, naming it LinearExample.
|
||||
solver = pywraplp.Solver('StiglerDietExample',
|
||||
|
||||
@@ -28,10 +28,10 @@ def solve_sudoku():
|
||||
cell = list(range(0, cell_size))
|
||||
|
||||
initial_grid = [[0, 6, 0, 0, 5, 0, 0, 2, 0], [0, 0, 0, 3, 0, 0, 0, 9, 0],
|
||||
[7, 0, 0, 6, 0, 0, 0, 1, 0], [0, 0, 6, 0, 3, 0, 4, 0, 0],
|
||||
[0, 0, 4, 0, 7, 0, 1, 0, 0], [0, 0, 5, 0, 9, 0, 8, 0, 0],
|
||||
[0, 4, 0, 0, 0, 1, 0, 0, 6], [0, 3, 0, 0, 0, 8, 0, 0, 0],
|
||||
[0, 2, 0, 0, 4, 0, 0, 5, 0]]
|
||||
[7, 0, 0, 6, 0, 0, 0, 1, 0], [0, 0, 6, 0, 3, 0, 4, 0, 0], [
|
||||
0, 0, 4, 0, 7, 0, 1, 0, 0
|
||||
], [0, 0, 5, 0, 9, 0, 8, 0, 0], [0, 4, 0, 0, 0, 1, 0, 0, 6],
|
||||
[0, 3, 0, 0, 0, 8, 0, 0, 0], [0, 2, 0, 0, 4, 0, 0, 5, 0]]
|
||||
|
||||
grid = {}
|
||||
for i in line:
|
||||
|
||||
@@ -27,252 +27,203 @@ def main():
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0
|
||||
],
|
||||
[
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1
|
||||
],
|
||||
[
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
|
||||
],
|
||||
[
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1,
|
||||
1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
|
||||
],
|
||||
[
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
|
||||
],
|
||||
[
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
|
||||
],
|
||||
[
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1,
|
||||
1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
|
||||
],
|
||||
[
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0
|
||||
],
|
||||
[
|
||||
0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
|
||||
],
|
||||
[
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
|
||||
],
|
||||
[
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1
|
||||
],
|
||||
[
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
|
||||
],
|
||||
[
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1
|
||||
],
|
||||
[
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0
|
||||
],
|
||||
[
|
||||
0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
|
||||
],
|
||||
[
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
|
||||
],
|
||||
[
|
||||
0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
|
||||
],
|
||||
[
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
|
||||
],
|
||||
[
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1,
|
||||
1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
|
||||
],
|
||||
[
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
|
||||
],
|
||||
[
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1,
|
||||
1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
|
||||
],
|
||||
[
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1,
|
||||
1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
|
||||
],
|
||||
[
|
||||
0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
|
||||
],
|
||||
[
|
||||
0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
|
||||
],
|
||||
[
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
|
||||
],
|
||||
[
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1
|
||||
],
|
||||
[
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
|
||||
],
|
||||
[
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
|
||||
],
|
||||
[
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0
|
||||
],
|
||||
[
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
|
||||
],
|
||||
[
|
||||
0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
|
||||
],
|
||||
[
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
|
||||
],
|
||||
[
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
|
||||
],
|
||||
[
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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|
||||
0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0
|
||||
], [
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0
|
||||
], [
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0
|
||||
], [
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0
|
||||
], [
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0
|
||||
], [
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0
|
||||
], [
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0
|
||||
]]
|
||||
|
||||
ntasks = len(available)
|
||||
nslots = len(available[0])
|
||||
|
||||
@@ -78,8 +78,7 @@ def tasks_and_workers_assignment_sat():
|
||||
model.Add(n == sum(x[i, j] for i in all_workers))
|
||||
c = model.NewIntVar(0, sum_of_costs * scaling,
|
||||
'sum_of_costs_of_group_%i' % j)
|
||||
model.Add(c == sum(
|
||||
y[k, j] * task_cost[k] * scaling for k in all_tasks))
|
||||
model.Add(c == sum(y[k, j] * task_cost[k] * scaling for k in all_tasks))
|
||||
a = model.NewIntVar(0, sum_of_costs * scaling,
|
||||
'average_cost_of_group_%i' % j)
|
||||
model.AddDivisionEquality(a, c, n)
|
||||
|
||||
@@ -147,8 +147,8 @@ class DataProblem():
|
||||
#######################
|
||||
def manhattan_distance(position_1, position_2):
|
||||
"""Computes the Manhattan distance between two points"""
|
||||
return (abs(position_1[0] - position_2[0]) +
|
||||
abs(position_1[1] - position_2[1]))
|
||||
return (
|
||||
abs(position_1[0] - position_2[0]) + abs(position_1[1] - position_2[1]))
|
||||
|
||||
|
||||
class CreateTimeEvaluator(object):
|
||||
@@ -165,9 +165,8 @@ class CreateTimeEvaluator(object):
|
||||
if from_node == to_node:
|
||||
travel_time = 0
|
||||
else:
|
||||
travel_time = manhattan_distance(
|
||||
data.locations[from_node],
|
||||
data.locations[to_node]) / data.vehicle.speed
|
||||
travel_time = manhattan_distance(data.locations[
|
||||
from_node], data.locations[to_node]) / data.vehicle.speed
|
||||
return travel_time
|
||||
|
||||
def __init__(self, data):
|
||||
@@ -181,8 +180,8 @@ class CreateTimeEvaluator(object):
|
||||
self._total_time[from_node][to_node] = 0
|
||||
else:
|
||||
self._total_time[from_node][to_node] = int(
|
||||
self.service_time(data, from_node) +
|
||||
self.travel_time(data, from_node, to_node))
|
||||
self.service_time(data, from_node) + self.travel_time(
|
||||
data, from_node, to_node))
|
||||
|
||||
def time_evaluator(self, from_node, to_node):
|
||||
"""Returns the total time between the two nodes"""
|
||||
@@ -209,8 +208,7 @@ def main():
|
||||
# Print Transit Time
|
||||
time_evaluator = CreateTimeEvaluator(data).time_evaluator
|
||||
print('Route 0:')
|
||||
print_transit_time([[0, 5], [5, 8], [8, 6], [6, 2], [2, 0]],
|
||||
time_evaluator)
|
||||
print_transit_time([[0, 5], [5, 8], [8, 6], [6, 2], [2, 0]], time_evaluator)
|
||||
|
||||
print('Route 1:')
|
||||
print_transit_time([[0, 9], [9, 14], [14, 16], [16, 10], [10, 0]],
|
||||
@@ -221,8 +219,7 @@ def main():
|
||||
time_evaluator)
|
||||
|
||||
print('Route 3:')
|
||||
print_transit_time([[0, 7], [7, 4], [4, 3], [3, 1], [1, 0]],
|
||||
time_evaluator)
|
||||
print_transit_time([[0, 7], [7, 4], [4, 3], [3, 1], [1, 0]], time_evaluator)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
||||
@@ -65,8 +65,8 @@ def create_data_model():
|
||||
#######################
|
||||
def manhattan_distance(position_1, position_2):
|
||||
"""Computes the Manhattan distance between two points"""
|
||||
return (abs(position_1[0] - position_2[0]) +
|
||||
abs(position_1[1] - position_2[1]))
|
||||
return (
|
||||
abs(position_1[0] - position_2[0]) + abs(position_1[1] - position_2[1]))
|
||||
|
||||
|
||||
def create_distance_evaluator(data):
|
||||
|
||||
@@ -39,9 +39,8 @@ class SolutionPrinter(cp_model.CpSolverSolutionCallback):
|
||||
print('Solution %i: ', self.__solution_count)
|
||||
print(' min vendors:', self.__min_vendors)
|
||||
for i in range(self.__num_vendors):
|
||||
print(
|
||||
' - vendor %i: ' % i, self.__possible_schedules[self.Value(
|
||||
self.__selected_schedules[i])])
|
||||
print(' - vendor %i: ' % i, self.__possible_schedules[self.Value(
|
||||
self.__selected_schedules[i])])
|
||||
print()
|
||||
|
||||
for j in range(self.__num_hours):
|
||||
@@ -73,12 +72,12 @@ def main():
|
||||
# Last columns are :
|
||||
# index_of_the_schedule, sum of worked hours (per work type).
|
||||
# The index is useful for branching.
|
||||
possible_schedules = [[1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 8],
|
||||
[1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 4],
|
||||
[0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 2, 5],
|
||||
[0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 3, 4],
|
||||
[1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 4, 3],
|
||||
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0]]
|
||||
possible_schedules = [[1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0,
|
||||
8], [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1,
|
||||
4], [0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 2,
|
||||
5], [0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 3, 4],
|
||||
[1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 4,
|
||||
3], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0]]
|
||||
|
||||
num_possible_schedules = len(possible_schedules)
|
||||
selected_schedules = []
|
||||
@@ -137,8 +136,8 @@ def main():
|
||||
print(' - conflicts : %i' % solver.NumConflicts())
|
||||
print(' - branches : %i' % solver.NumBranches())
|
||||
print(' - wall time : %f s' % solver.WallTime())
|
||||
print(' - number of solutions found: %i' %
|
||||
solution_printer.solution_count())
|
||||
print(
|
||||
' - number of solutions found: %i' % solution_printer.solution_count())
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
||||
@@ -65,8 +65,8 @@ def create_data_model():
|
||||
#######################
|
||||
def manhattan_distance(position_1, position_2):
|
||||
"""Computes the Manhattan distance between two points"""
|
||||
return (abs(position_1[0] - position_2[0]) +
|
||||
abs(position_1[1] - position_2[1]))
|
||||
return (
|
||||
abs(position_1[0] - position_2[0]) + abs(position_1[1] - position_2[1]))
|
||||
|
||||
|
||||
def create_distance_evaluator(data):
|
||||
|
||||
@@ -65,8 +65,8 @@ def create_data_model():
|
||||
#######################
|
||||
def manhattan_distance(position_1, position_2):
|
||||
"""Computes the Manhattan distance between two points"""
|
||||
return (abs(position_1[0] - position_2[0]) +
|
||||
abs(position_1[1] - position_2[1]))
|
||||
return (
|
||||
abs(position_1[0] - position_2[0]) + abs(position_1[1] - position_2[1]))
|
||||
|
||||
|
||||
def create_distance_evaluator(data):
|
||||
|
||||
@@ -88,23 +88,23 @@ def BuildData():
|
||||
|
||||
# Connection matrix: who knows who, and how strong
|
||||
# is the relation
|
||||
C = [[1, 50, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],
|
||||
[50, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],
|
||||
[1, 1, 1, 50, 1, 1, 1, 1, 10, 0, 0, 0, 0, 0, 0, 0, 0],
|
||||
[1, 1, 50, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],
|
||||
[1, 1, 1, 1, 1, 50, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],
|
||||
[1, 1, 1, 1, 50, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],
|
||||
[1, 1, 1, 1, 1, 1, 1, 50, 1, 0, 0, 0, 0, 0, 0, 0, 0],
|
||||
[1, 1, 1, 1, 1, 1, 50, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],
|
||||
[1, 1, 10, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],
|
||||
[0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 50, 1, 1, 1, 1, 1, 1],
|
||||
[0, 0, 0, 0, 0, 0, 0, 0, 0, 50, 1, 1, 1, 1, 1, 1, 1],
|
||||
[0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1],
|
||||
[0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1],
|
||||
[0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1],
|
||||
[0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1],
|
||||
[0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1],
|
||||
[0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1]]
|
||||
C = [[1, 50, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0,
|
||||
0], [50, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0,
|
||||
0], [1, 1, 1, 50, 1, 1, 1, 1, 10, 0, 0, 0, 0, 0, 0, 0,
|
||||
0], [1, 1, 50, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],
|
||||
[1, 1, 1, 1, 1, 50, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0,
|
||||
0], [1, 1, 1, 1, 50, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0,
|
||||
0], [1, 1, 1, 1, 1, 1, 1, 50, 1, 0, 0, 0, 0, 0, 0, 0, 0],
|
||||
[1, 1, 1, 1, 1, 1, 50, 1, 1, 0, 0, 0, 0, 0, 0, 0,
|
||||
0], [1, 1, 10, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0,
|
||||
0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 50, 1, 1, 1, 1, 1, 1],
|
||||
[0, 0, 0, 0, 0, 0, 0, 0, 0, 50, 1, 1, 1, 1, 1, 1,
|
||||
1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1,
|
||||
1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1], [
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1
|
||||
], [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1], [
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1
|
||||
], [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1]]
|
||||
|
||||
# Names of the guests. B: Bride side, G: Groom side
|
||||
names = [
|
||||
@@ -133,8 +133,8 @@ def solve_with_discrete_model():
|
||||
seats = {}
|
||||
for t in all_tables:
|
||||
for g in all_guests:
|
||||
seats[(t, g)] = model.NewBoolVar(
|
||||
"guest %i seats on table %i" % (g, t))
|
||||
seats[(t, g)] = model.NewBoolVar("guest %i seats on table %i" % (g,
|
||||
t))
|
||||
|
||||
colocated = {}
|
||||
for g1 in range(num_guests - 1):
|
||||
@@ -151,8 +151,9 @@ def solve_with_discrete_model():
|
||||
|
||||
# Objective
|
||||
model.Maximize(
|
||||
sum(C[g1][g2] * colocated[g1, g2] for g1 in range(num_guests - 1)
|
||||
for g2 in range(g1 + 1, num_guests) if C[g1][g2] > 0))
|
||||
sum(C[g1][g2] * colocated[g1, g2]
|
||||
for g1 in range(num_guests - 1) for g2 in range(g1 + 1, num_guests)
|
||||
if C[g1][g2] > 0))
|
||||
|
||||
#
|
||||
# Constraints
|
||||
@@ -186,17 +187,17 @@ def solve_with_discrete_model():
|
||||
# Min known neighbors rule.
|
||||
for t in all_tables:
|
||||
model.Add(
|
||||
sum(same_table[(g1, g2, t)] for g1 in range(num_guests - 1)
|
||||
for g2 in range(g1 + 1, num_guests)
|
||||
for t in all_tables if C[g1][g2] > 0) >= min_known_neighbors)
|
||||
sum(same_table[(g1, g2, t)]
|
||||
for g1 in range(num_guests - 1)
|
||||
for g2 in range(g1 + 1, num_guests) for t in all_tables
|
||||
if C[g1][g2] > 0) >= min_known_neighbors)
|
||||
|
||||
# Symmetry breaking. First guest seats on the first table.
|
||||
model.Add(seats[(0, 0)] == 1)
|
||||
|
||||
### Solve model.
|
||||
solver = cp_model.CpSolver()
|
||||
solution_printer = WeddingChartPrinter(seats, names, num_tables,
|
||||
num_guests)
|
||||
solution_printer = WeddingChartPrinter(seats, names, num_tables, num_guests)
|
||||
solver.SolveWithSolutionCallback(model, solution_printer)
|
||||
|
||||
print("Statistics")
|
||||
|
||||
@@ -19,14 +19,13 @@ def schedule():
|
||||
|
||||
# Input data.
|
||||
positions = [
|
||||
1, 2, 8, 10, 5, 3, 4, 3, 6, 6, 4, 5, 4, 3, 4, 4, 3, 4, 2, 1, 0, 0, 0,
|
||||
0, 1, 2, 9, 9, 4, 3, 4, 3, 5, 4, 5, 2, 5, 6, 6, 7, 4, 2, 1, 0, 0, 0, 0,
|
||||
0, 0, 2, 7, 6, 5, 2, 4, 4, 6, 6, 4, 5, 5, 5, 7, 5, 4, 4, 2, 3, 1, 0, 0,
|
||||
0, 1, 2, 9, 7, 2, 2, 4, 2, 4, 5, 3, 2, 6, 7, 5, 6, 4, 4, 2, 1, 0, 0, 0,
|
||||
0, 2, 2, 8, 8, 6, 3, 3, 3, 10, 9, 6, 3, 3, 4, 5, 4, 5, 4, 2, 1, 0, 0,
|
||||
0, 0, 1, 2, 9, 5, 5, 4, 5, 2, 5, 7, 5, 3, 4, 8, 4, 4, 2, 3, 1, 0, 0, 0,
|
||||
0, 0, 1, 2, 10, 5, 5, 4, 5, 2, 4, 6, 7, 4, 4, 5, 4, 4, 3, 3, 2, 1, 0,
|
||||
0, 0, 0
|
||||
1, 2, 8, 10, 5, 3, 4, 3, 6, 6, 4, 5, 4, 3, 4, 4, 3, 4, 2, 1, 0, 0, 0, 0,
|
||||
1, 2, 9, 9, 4, 3, 4, 3, 5, 4, 5, 2, 5, 6, 6, 7, 4, 2, 1, 0, 0, 0, 0, 0,
|
||||
0, 2, 7, 6, 5, 2, 4, 4, 6, 6, 4, 5, 5, 5, 7, 5, 4, 4, 2, 3, 1, 0, 0, 0,
|
||||
1, 2, 9, 7, 2, 2, 4, 2, 4, 5, 3, 2, 6, 7, 5, 6, 4, 4, 2, 1, 0, 0, 0, 0,
|
||||
2, 2, 8, 8, 6, 3, 3, 3, 10, 9, 6, 3, 3, 4, 5, 4, 5, 4, 2, 1, 0, 0, 0, 0,
|
||||
1, 2, 9, 5, 5, 4, 5, 2, 5, 7, 5, 3, 4, 8, 4, 4, 2, 3, 1, 0, 0, 0, 0, 0,
|
||||
1, 2, 10, 5, 5, 4, 5, 2, 4, 6, 7, 4, 4, 5, 4, 4, 3, 3, 2, 1, 0, 0, 0, 0
|
||||
]
|
||||
|
||||
possible_shifts = [[
|
||||
@@ -38,103 +37,79 @@ def schedule():
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 40
|
||||
],
|
||||
[
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1,
|
||||
1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
|
||||
1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 40
|
||||
],
|
||||
[
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1,
|
||||
1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1,
|
||||
1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 40
|
||||
],
|
||||
[
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
|
||||
1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1,
|
||||
1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1,
|
||||
1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 40
|
||||
],
|
||||
[
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0
|
||||
],
|
||||
[
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 16
|
||||
],
|
||||
[
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
|
||||
1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 6, 16
|
||||
],
|
||||
[
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 7, 16
|
||||
],
|
||||
[
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1,
|
||||
1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1,
|
||||
1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 8, 40
|
||||
]]
|
||||
], [
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
1, 40
|
||||
], [
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
2, 40
|
||||
], [
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
3, 40
|
||||
], [
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
4, 0
|
||||
], [
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
5, 16
|
||||
], [
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
6, 16
|
||||
], [
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1,
|
||||
7, 16
|
||||
], [
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1,
|
||||
8, 40
|
||||
]]
|
||||
|
||||
# Useful numbers.
|
||||
num_slots = len(positions)
|
||||
|
||||
Reference in New Issue
Block a user