first shot

CostGreedy.txt

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+100
+100
+3
+0
+A; 0; 1; 1; 0
+B; 1; 99; 99; 0 
+C; 2; 100; 100; 200 

WeightGreedy.txt

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+100
+100
+3
+0
+A; 0; 1; 100; -10
+B; 1; 99; 100; -10 
+C; 2; 100; 0; 100 

easy2

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+100
+100
+2
+0
+somewhat_heavy_rock; 0; 90; 1; 5
+gem_on_sale; 1; 2; 50; 200

easy3

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+100
+100
+3
+3
+somewhat_heavy_rock; 0; 90; 1; 5
+gem_on_sale; 1; 2; 50; 200
+small_present; 2; 1; 20; 40
+0, 1
+1, 2
+0, 2

easy4

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+100
+100
+3
+4
+somewhat_heavy_rock; 0; 90; 1; 5
+gem_on_sale; 1; 2; 50; 200
+small_present; 2; 1; 20; 40
+1, 10
+2, 11
+3, 12
+1, 2, 3, 4

example

@@ -0,0 +1,7 @@
+100
+100
+2
+1
+heavy_worthless_rock; 0; 99; 1; 5
+gem_on_sale; 1; 2; 50; 200
+0, 1

generate_items.py

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+#!/usr/bin/env python
+from __future__ import division
+import argparse
+import random
+import numpy as np
+
+def generate_items():
+    items = []
+    classes = set()
+    maxWeight = 0
+    maxCost = 0
+    for i in range(20):
+        item_name = "Item " + str(i)
+        class_name = random.randint(0, 199999)
+        weight = random.randint(0, 100000)
+        cost = random.randint(0, 100000)
+        resale = check_profit_positive(cost, random.randint(0, 100000))
+        item_string = "{}; {}; {}; {}; {}".format(item_name, class_name, weight, cost, resale)
+        items.append(item_string)
+        classes.add(class_name)
+
+        if maxWeight < weight:
+            maxWeight = weight
+        if maxCost < cost:
+            maxCost = cost
+    return (items, list(classes), maxWeight, maxCost)
+
+def check_profit_positive(cost, resale):
+    while resale < cost:
+        resale = random.randint(0, 100000)
+    return resale
+
+def write_output(filename, items_chosen):
+  with open(filename, "w") as f:
+    for i in items_chosen:
+      f.write("{0}\n".format(i))
+
+#3 1's, 20 2's, 12 3's, 36 4's = 71
+def generate_items_only_one_class_chosen():
+    items = []
+    classes = set()
+
+    maxWeight = 0
+    maxCost = 0
+
+    for i in range(3): #Three items of class 1
+        item_name = "Item " + str(i)
+        class_name = 1
+        weight = random.randint(0, 100000)
+        cost = random.randint(0, 100000)
+        resale = check_profit_positive(cost, random.randint(0, 100000))
+        item_string = "{}; {}; {}; {}; {}".format(item_name, class_name, weight, cost, resale)
+        items.append(item_string)
+        classes.add(class_name)
+
+        if maxWeight < weight:
+            maxWeight = weight
+        if maxCost < cost:
+            maxCost = cost
+
+    for i in range(20): #Twenty items of class 2
+        item_name = "Item " + str(i + 3)
+        class_name = 2
+        weight = random.randint(0, 100000)
+        cost = random.randint(0, 100000)
+        resale = check_profit_positive(cost, random.randint(0, 100000))
+        item_string = "{}; {}; {}; {}; {}".format(item_name, class_name, weight, cost, resale)
+        items.append(item_string)
+        classes.add(class_name)
+
+        if maxWeight < weight:
+            maxWeight = weight
+        if maxCost < cost:
+            maxCost = cost
+
+    for i in range(12): #Twelve items of class 3
+        item_name = "Item " + str(i + 23)
+        class_name = 3
+        weight = random.randint(0, 100000)
+        cost = random.randint(0, 100000)
+        resale = check_profit_positive(cost, random.randint(0, 100000))
+        item_string = "{}; {}; {}; {}; {}".format(item_name, class_name, weight, cost, resale)
+        items.append(item_string)
+        classes.add(class_name)
+
+        if maxWeight < weight:
+            maxWeight = weight
+        if maxCost < cost:
+            maxCost = cost
+
+    for i in range(36): #36 items of class 4
+        item_name = "Item " + str(i + 35)
+        class_name = 4
+        weight = random.randint(0, 100000)
+        cost = random.randint(0, 100000)
+        resale = check_profit_positive(cost, random.randint(0, 100000))
+        item_string = "{}; {}; {}; {}; {}".format(item_name, class_name, weight, cost, resale)
+        items.append(item_string)
+        classes.add(class_name)
+
+        if maxWeight < weight:
+            maxWeight = weight
+        if maxCost < cost:
+            maxCost = cost
+
+    for i in range(29): #100 - 71 = 29
+        item_name = "Item " + str(i + 71)
+        class_name = i + 71
+        weight = random.randint(0, 100000)
+        cost = random.randint(0, 100000)
+        resale = check_profit_positive(cost, random.randint(0, 100000))
+        item_string = "{}; {}; {}; {}; {}".format(item_name, class_name, weight, cost, resale)
+        items.append(item_string)
+        classes.add(class_name)
+
+        if maxWeight < weight:
+            maxWeight = weight
+        if maxCost < cost:
+            maxCost = cost
+
+    return (items, list(classes), maxWeight, maxCost)
+
+def createConstraints(classes):
+  # Generate random number of elements for the constraint
+  allConstraints = []
+  for i in range(8):
+    num = np.random.randint(2,10)
+    a = [classes[np.random.randint(0, len(classes))] for i in range(num)]
+    allConstraints.append(a)
+
+  constraints_formatted = []
+  for constraint in allConstraints:
+    constraints_formatted.append(', '.join(map(str, constraint)))
+  return constraints_formatted
+
+if __name__ == "__main__":
+
+  parser = argparse.ArgumentParser(description="PickItems solver.")
+  parser.add_argument("output_file", type=str, help="____.out")
+  args = parser.parse_args()
+
+  items_chosen, classes_chosen, maxWeight, maxCost = generate_items()
+  #items_chosen, classes_chosen, maxWeight, maxCost = generate_items_only_one_class_chosen()
+  constraints = createConstraints(classes_chosen)
+
+  pounds = [maxWeight]
+  dollars = [maxCost]
+  num_items = [20]
+  num_constraints = [8]
+
+  setup_values = pounds + dollars + num_items + num_constraints
+  write_to_file = setup_values + items_chosen + constraints
+
+  for i in range(5):
+      random.shuffle(constraints)
+      write_to_file += ["", "New constraint " + str(i)] + constraints
+
+  write_output(args.output_file, write_to_file)

knapsack.py

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+## Aiswarya Sankar
+## 4/21/17
+## Knapsack Implementation
+
+import numpy as np 
+
+def knapsack():
+	noItems = int(input())
+	totalWeight = int(input())
+	weights = int(input().split(','))
+	values = int(input().split(','))
+
+
+	table = np.zeros(totalWeight * noItems).reshape((totalWeight, noItems))
+	for item in noItems:
+		for weight in totalWeight:
+			pass
+
+
+knapsack()
+
+
+
+#code
+import numpy as np
+
+def knapsack():
+    numTests = input()
+    for num in numTests:
+        noItems = int(input())
+        totalWeight = int(input())
+        weights = input().split(' ')
+        values = input().split(' ')
+
+    table = []
+
+    for i in range(noItems):
+        table[i][0] = 0
+    for j in range(totalWeight):
+        table[0][j] = 0
+
+    for i in range(noItems):
+        for j in range(totalWeight):
+            if weights[j] + table[i][j-1] > totalWeight:
+                table[i][j] = table[i][j-1]
+            else:
+                table[i][j] = max(table[i-1][j-weights[j]] + value[j], table[i-1][j])
+    return table[noItems][totalWeight]
+
+knapsack()

maxProfit.txt

@@ -0,0 +1,9 @@
+5
+5
+4
+1
+Item1; 2; 2; 2; 10
+Item2; 2; 3; 3; 10
+Item3; 1; 5; 10; 1
+Item4; 1; 5; 10; 1
+1, 2

minCostTest.txt

@@ -0,0 +1,9 @@
+5
+5
+4
+1
+Item1; 2; 2; 2; 1
+Item2; 2; 3; 3; 1
+Item3; 1; 5; 10; 1
+Item4; 1; 5; 10; 1
+1, 2

numItemsTest.txt

@@ -0,0 +1,8 @@
+5
+5
+3
+1
+Item1; 2; 2; 2; 1
+Item2; 2; 3; 3; 1
+Item3; 1; 5; 5; 1
+1, 2

o.txt

@@ -0,0 +1 @@
+1

out

@@ -0,0 +1,82 @@
+98294
+98270
+20
+8
+Item 0; 45727; 63940; 26022; 96465
+Item 1; 182050; 4706; 86454; 97068
+Item 2; 72054; 50924; 91329; 97618
+Item 3; 139740; 30198; 6622; 54366
+Item 4; 887; 66040; 27617; 29627
+Item 5; 161899; 98294; 12259; 42096
+Item 6; 158273; 30005; 82965; 92575
+Item 7; 57402; 23286; 29058; 86304
+Item 8; 49895; 98107; 40745; 41927
+Item 9; 12351; 78798; 7666; 76225
+Item 10; 198597; 34365; 22121; 94027
+Item 11; 151413; 6748; 79992; 99981
+Item 12; 52337; 88667; 66612; 80544
+Item 13; 63064; 87849; 75066; 94733
+Item 14; 176764; 76031; 9802; 49767
+Item 15; 73601; 86192; 30600; 79100
+Item 16; 106649; 35134; 82336; 82905
+Item 17; 181691; 79490; 66521; 87161
+Item 18; 139823; 48882; 32872; 53729
+Item 19; 43291; 13058; 98270; 99689
+139740, 176764, 182050
+45727, 57402
+106649, 151413
+139823, 72054, 106649
+63064, 63064, 198597
+45727, 49895, 12351, 158273, 49895, 57402, 139740
+43291, 57402, 176764, 43291, 182050, 45727, 49895
+181691, 106649, 887, 198597
+
+New constraint 0
+43291, 57402, 176764, 43291, 182050, 45727, 49895
+139823, 72054, 106649
+45727, 57402
+139740, 176764, 182050
+181691, 106649, 887, 198597
+63064, 63064, 198597
+106649, 151413
+45727, 49895, 12351, 158273, 49895, 57402, 139740
+
+New constraint 1
+43291, 57402, 176764, 43291, 182050, 45727, 49895
+139740, 176764, 182050
+106649, 151413
+181691, 106649, 887, 198597
+139823, 72054, 106649
+45727, 49895, 12351, 158273, 49895, 57402, 139740
+63064, 63064, 198597
+45727, 57402
+
+New constraint 2
+181691, 106649, 887, 198597
+45727, 49895, 12351, 158273, 49895, 57402, 139740
+43291, 57402, 176764, 43291, 182050, 45727, 49895
+106649, 151413
+63064, 63064, 198597
+45727, 57402
+139823, 72054, 106649
+139740, 176764, 182050
+
+New constraint 3
+45727, 49895, 12351, 158273, 49895, 57402, 139740
+139740, 176764, 182050
+45727, 57402
+63064, 63064, 198597
+181691, 106649, 887, 198597
+139823, 72054, 106649
+43291, 57402, 176764, 43291, 182050, 45727, 49895
+106649, 151413
+
+New constraint 4
+106649, 151413
+139823, 72054, 106649
+181691, 106649, 887, 198597
+63064, 63064, 198597
+43291, 57402, 176764, 43291, 182050, 45727, 49895
+45727, 57402
+139740, 176764, 182050
+45727, 49895, 12351, 158273, 49895, 57402, 139740

out1

@@ -0,0 +1 @@
+1

phase1.py

@@ -0,0 +1,8 @@
+## Aiswarya Sankar
+## 4/16/2017
+## Phase1.py 
+
+## This program will enforce that each constraint is satisfied in the problem.  This means that you will never have a variable in one of the sets that you actually can't choose any values from.
+
+def check_constraints():
+