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import pulp | |
import pandas as pd | |
import numpy as np | |
"""Example drivers.csv file: | |
driver_id,lat,lon | |
dr_1,10,20 | |
dr_2,30,50 | |
dr_3,50,40 | |
dr_4,80,20 |
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# 1st approach: | |
def kth_largest(arr, k): | |
for i in range(k-1): | |
arr.remove(max(arr)) | |
return max(arr) | |
# 2nd approach: | |
def kth_largest(arr, k): | |
n = len(arr) |
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import pandas as pd | |
import numpy as np | |
import matplotlib.pyplot as plt | |
def update_annot(ind): | |
pos = sc.get_offsets()[ind["ind"][0]] | |
annot.xy = pos | |
attributes_to_show = [0, 1, 2, 7] | |
text = df.iloc[ind["ind"][0], attributes_to_show].to_string() |
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from typing import List, Any | |
class Node: | |
def __init__(self, key, value, nxt=None): | |
self.key: str = key | |
self.value: Any = value | |
self.nxt: Node = nxt | |
class LinkedList: |
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import numpy as np | |
def approximate_pi(n): | |
points = np.random.uniform(-1, 1, (n, 2)) | |
inside = np.sum(points[:,0]**2+points[:,1]**2 <= 1) | |
k = inside/n | |
return 4*k | |
n = 1000000 |
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import numpy as np | |
import matplotlib.pyplot as plt | |
def midpoint(p1, p2): | |
x1, y1 = p1 | |
x2, y2 = p2 | |
return (x1+x2)/2, (y1+y2)/2 |
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def is_valid(grid, r, c, k): | |
not_in_row = k not in grid[r] | |
not_in_column = k not in [grid[i][c] for i in range(9)] | |
not_in_box = k not in [grid[i][j] for i in range(r//3*3, r//3*3+3) for j in range(c//3*3, c//3*3+3)] | |
return not_in_row and not_in_column and not_in_box | |
def solve(grid, r=0, c=0): | |
if r == 9: | |
return True |
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def is_safe(board, i, j): | |
n = len(board) | |
j_left = j | |
j_right = j | |
while i >= 0: | |
if (j_left >= 0 and board[i][j_left] == 1) or board[i][j] == 1 or (j_right < n and board[i][j_right] == 1): | |
return False | |
i -= 1 | |
j_left -= 1 | |
j_right += 1 |
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def knapsack(values, weights, k, i=0, lookup=None): | |
lookup = {} if lookup is None else lookup | |
if (i, k) in lookup: | |
return lookup[(i, k)] | |
if i == len(values): | |
return 0 | |
elif k < 0: | |
return float('-inf') | |
else: | |
lookup[(i, k)] = max(values[i]+knapsack(values, weights, k-weights[i], i+1, lookup), |
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import math | |
def dist(p1, p2): | |
x1, y1, x2, y2 = *p1, *p2 | |
return math.sqrt((y2-y1)**2 + (x2-x1)**2) | |
def polar_angle(p1, p2): | |
if p1[1] == p2[1]: | |
return -math.pi |
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