Added missing classes from final year at OSU

OSU Coursework/CS 325 - Analysis of Algorithms/Julie Schuffort - Passed/Homework 8/README.txt

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+To run this program, place the file bin.txt in the same directory, then run
+
+python3 binpack.py

OSU Coursework/CS 325 - Analysis of Algorithms/Julie Schuffort - Passed/Homework 8/bin.txt

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+3
+10
+6
+5 10 2 5 4 4
+20
+19
+9 10 2 1 7 3 3 3 3 14 6 5 2 8 6 4 6 2 6
+10
+20
+4 4 4 4 4 4 4 4 4 4 6 6 6 6 6 6 6 6 6 6
+
+

OSU Coursework/CS 325 - Analysis of Algorithms/Julie Schuffort - Passed/Homework 8/binpack.py

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+from time import time
+from collections import deque
+from copy import copy
+
+INPUT_FILENAME = "bin.txt"
+
+
+class BinPacker(object):
+    def __init__(self, bin_pack_test_case):
+        self._bin_size = bin_pack_test_case["bin_capacity"]
+        self._items = deque(list(bin_pack_test_case["items"]))
+        self._bins = []
+        self._run_time = 0
+
+    def _solve(self):
+        raise NotImplemented
+
+    def solve_and_time(self):
+        start_time = time() * 1000
+        self._solve()
+        self._run_time = (time() * 1000) - start_time
+
+        return len(self._bins), self._run_time
+
+
+class FirstFit(BinPacker):
+    def _solve(self):
+        while self._items:
+            current_item = self._items.popleft()
+
+            item_placed = False
+
+            for bin_index in range(len(self._bins)):
+                new_sum = self._bins[bin_index] + current_item
+
+                if new_sum <= self._bin_size:
+                    self._bins[bin_index] = new_sum
+                    item_placed = True
+                    break
+
+            if not item_placed:
+                self._bins.append(current_item)
+
+
+class FirstFitDecreasing(FirstFit):
+    def _solve(self):
+        sorted_descending = list(self._items)
+        sorted_descending.sort(reverse=True)
+        self._items = deque(sorted_descending)
+
+        super(FirstFitDecreasing, self)._solve()
+
+
+class BestFit(BinPacker):
+    def _solve(self):
+        while self._items:
+            current_item = self._items.popleft()
+
+            min_room_left_over = self._bin_size
+            best_index_sum = None
+            best_index = None
+
+            for bin_index in range(len(self._bins)):
+                new_sum = self._bins[bin_index] + current_item
+                bin_difference = self._bin_size - new_sum
+
+                if 0 <= bin_difference < min_room_left_over:
+
+                    best_index = bin_index
+                    best_index_sum = new_sum
+                    min_room_left_over = bin_difference
+
+            if best_index is not None:
+                self._bins[best_index] = best_index_sum
+            else:
+                self._bins.append(current_item)
+
+        # print(self._bins)
+
+
+def get_test_cases_from_file(filename):
+    input_test_cases = []
+
+    with open(filename, "r") as input_file:
+        file_lines = input_file.readlines()
+
+        current_line = 0
+        num_test_cases = int(file_lines[current_line])
+
+        for _ in range(num_test_cases):
+            current_test_case = {}
+
+            current_line += 1
+            current_test_case["bin_capacity"] = int(file_lines[current_line])
+
+            current_line += 1
+            num_items = int(file_lines[current_line])
+
+            current_line += 1
+            items_split = file_lines[current_line].split(" ")
+            current_test_case["items"] = [int(item_str) for item_str in items_split[:num_items]]
+
+            input_test_cases.append(current_test_case)
+
+        return input_test_cases
+
+
+if __name__ == "__main__":
+    test_cases = get_test_cases_from_file(INPUT_FILENAME)
+
+    for test_index, test_case in enumerate(test_cases):
+        first_fit_num_bins, first_fit_run_time = FirstFit(test_case).solve_and_time()
+        first_fit_dec_num_bins, first_fit_dec_run_time = FirstFitDecreasing(test_case).solve_and_time()
+        best_fit_num_bins, best_fit_run_time = BestFit(test_case).solve_and_time()
+
+        print("Test Case {} First Fit: {}, {} ms. First Fit Decreasing {}, {} ms. Best Fit: {}, {} ms.".format(
+            test_index + 1,
+            first_fit_num_bins, first_fit_run_time,
+            first_fit_dec_num_bins, first_fit_dec_run_time,
+            best_fit_num_bins, best_fit_run_time
+        ))