danong

TEMPLATE = app
CONFIG += console
CONFIG -= app_bundle
CONFIG -= qt

SOURCES += main.cpp \
    polygon.cpp

HEADERS += \
    vertex.h \

danong

import random
import numpy as np
# for graphing
from mpl_toolkits.mplot3d.axes3d import Axes3D
import matplotlib.pyplot as plt

if __name__ == "__main__":
    # setting up scatterplot
    Xa = []
    Ya = []

danong

import random

if __name__ == "__main__":
    #  define and initialize queue variables
    lambd_in = 0.5
    lambd_out = 0.4
    closing_time = 100
    t = 0
    num_arrivals = 0
    num_departures = 0

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"""
EXAMPLES
Puzzle 1:
[0, 1, 4]
[2, 3, 5]
[8, 7, 6]
Longest chain is [4, 5, 6, 7, 8] with length 5.

Puzzle 2:
[0, 2]

danong

class Node:
    def __init__(self, val, left=None, right=None):
        self.val = val
        self.left = left
        self.right = right


def is_bst_rec(node: Node, min_v: object = None, max_v: object = None) -> bool: # todo should use float('inf') and float('-inf')
    if (min_v and node.val < min_v) or (max_v and node.val > max_v):
        return False

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class Solution:
    def setZeroes(self, matrix):
        """
        :type matrix: List[List[int]]
        :rtype: void Do not return anything, modify matrix in-place instead.
        """
        row_0 = col_0 = False
        # step 1: iterate through matrix and set 0 markers in 1st row and col (except top left)
        for row_idx, row in enumerate(matrix):
            for col_idx, val in enumerate(row):

danong

# coding=utf-8
"""Sudoku solver using constraint propogation + search"""
from collections import defaultdict
from itertools import chain, product
from pprint import pprint
from typing import List

CELLS = None
UNITS = None
PEERS = None

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def detect_cycles(graph: dict) -> bool:
    """Return whether or not graph has any cycles."""
    visited = set()
    path = set()

    def visit(node):
        """Return True if node is in a cycle"""
        if node in visited:
            return False
        visited.add(node)

danong

# 8 Tile Solver
# Written by Daniel Ong for COEN 166: Artificial Intelligence
#
# A comparison of the real time taken to solve an n tile puzzle using:
# 1. Iterative deepening depth-first search
# 2. Depth-first search
# 3. Breadth-first search
# Tested on the 8 tile puzzle
# Some code adapted from Edd Mann's blog post: http://eddmann.com/posts/using-iterative-deepening-depth-first-search-in-python/