Homeowrk from pc to mac
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python
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# import matplotlib.pyplot as plt
import numpy as np
import sqlite3
import random as rn
from scipy.stats import pearsonr
from sklearn.cluster import KMeans
# problem 1
#translates a random int into a step along the random walk
#parameters: int i for the step index, numpy array x for tracking the left/right location at index i, parameter constant is for our internal use.
#numpy array y for tracking the forward/backward location at index i
#return: none
# Don't change the parameter "constant", you have to only work with x, y and i.
# Comment the import of matplotlib before submitting to the Autograder
def step(x, y, i, constant):
rn.seed(constant)
direction = rn.randint(1,4) #leave this
if direction == 1:
x[i] = x[i-1] + 1
y[i] = y[i-1]
elif direction == 2:
x[i] = x[i-1] - 1
y[i] = y[i-1]
elif direction == 3:
y[i] = y[i-1] + 1
x[i] = x[i-1]
elif direction == 4:
y[i] = y[i-1] - 1
x[i] = x[i-1]
return (x,y)
#Do not change -- visualization
# def graphit(x,y,n):
# plt.title("Random {0} Walk\nLast Location {1},{2}".format(n,int(x[n-1]),int(y[n-1])) )
# plt.plot(x,y)
# plt.plot([x[1],x[1]],[y[1]-10,y[1]+10], "b-")
# plt.plot([x[1]-10,x[1]+10],[y[1],y[1]], "b-")
# plt.plot([x[n-1]-10,x[n-1]+10],[y[n-1],y[n-1]], "r-")
# plt.plot([x[n-1],x[n-1]],[y[n-1]-10,y[n-1]+10], "r-")
# plt.show()
#Problem 2
# Replace the pass statements with correct code implementation
class Line:
def __init__(self, **kwargs):
self.m = None
self.b = None
self.fn = None
if 'points' in kwargs:
x1 = kwargs['points'][0][0]
x2 = kwargs['points'][1][0]
y1 = kwargs["points"][0][1]
y2 = kwargs['points'][1][1]
self.m = (y2 - y1) / (x2-x1)
self.b = y1 - self.m * x1
elif "point_intercept" in kwargs:
x1 = kwargs["point_intercept"][0][0]
y1 = kwargs["point_intercept"][0][1]
self.b = kwargs["point_intercept"][1]
x2 = 0
y2 = self.b
self.m = (y1 - y2) / (x1-x2)
elif 'function' in kwargs:
function = kwargs['function'].replace(" ","")
function = function.split('x')
if function[0] == '-':
self.m = -1
elif function[0] == "":
self.m = 1
else:
self.m = float(function[0])
if len(function) == 1:
self.b = 0
else:
self.b = float(function[1])
elif 'point_slope' in kwargs:
x1 = kwargs['point_slope'][0][0]
y1 = kwargs['point_slope'][0][1]
self.m = kwargs['point_slope'][1]
self.b = (-self.m*x1) + y1
self.build_lambda()
def build_lambda(self):
self.fn = lambda x: (self.m*x) + self.b
def sign(self):
if self.b > 0:
return f"+ {round(self.b,2)}"
elif self.b < 0:
return f"- {round(abs(self.b),2)}"
else:
return ""
def get_slope_intercept(self):
return self.m,self.b
def __and__(self, right_line):
if self.m == right_line.m:
if self.b == right_line.b:
return "same"
else: return "parallel"
else:
xintercept = (right_line.b - self.b) / (self.m - right_line.m)
yintercept = self.fn(xintercept)
return (xintercept,yintercept)
def __call__(self, arg):
return self.fn(arg)
def __str__(self):
return f"y = {'' if self.m == 1 else round(self.m,2)}x {self.sign()}"
#Problem 3
# We have provided the file "mydatabase.db" with the HW.
# When submitting to the Autograder, use as as i.e. sqlite3.connect("mydatabase.db") function.
# Some tips:
# When testing on your system, if the sqlite3.connect("mydatabase.db") does not work because Python can't find the database file then you may need to
# provide the absolute path i.e. sqlite3.connect("replace_with_absolute_path/mydatabase.db")
# We encourage some testing to figure it out.
# Remember that on Windows - the path use two back slashes \\, while on MAC and Linux the path use forward slash /
connection = sqlite3.connect("mydatabase.db")
my_cursor = connection.cursor()
my_cursor.execute("""SELECT name FROM sqlite_master WHERE type='table'""")
if not (list(my_cursor.fetchall())):
# print('Building Weather table...')
my_cursor.execute(''' CREATE TABLE Weather (City text, State text, High real, Low real)''')
my_cursor.execute("INSERT INTO Weather Values('Phoenix', 'Arizona', 105, 90)")
my_cursor.execute("INSERT INTO Weather Values('Tucson', 'Arizona', 101, 92)")
my_cursor.execute("INSERT INTO Weather Values('Flag Staff', 'Arizona', 105, 90)")
my_cursor.execute("INSERT INTO Weather Values('San Diego', 'California', 77, 60)")
my_cursor.execute("INSERT INTO Weather Values('Albuquerque', 'New Mexico', 80, 72)")
my_cursor.execute("INSERT INTO Weather Values('Nome', 'Alaska', 64 ,-54)")
else:
print('Weather Table Exists')
data = [
('Phoenix', 'Arizona', 105, 90),('Tucson', 'Arizona', 101, 92),
('Flag Staff', 'Arizona', 105, 90), ('San Diego', 'California', 77, 60),
('Alguquerque', 'New Mexico', 80, 72), ('Nome', 'Alaska', 64 ,-54)
]
# We have completed query1 for you.
def query1(db_cursor):
temp = []
for i in db_cursor.execute("SELECT * FROM Weather"):
temp.append[i]
return temp
def query2(db_cursor):
pass
def query3(db_cursor):
pass
def query4(db_cursor):
pass
def query5(db_cursor):
pass
def query6(db_cursor):
pass
def query7(db_cursor):
pass
# problem 4
def correlation4(data):
output = []
col = []
headers= data[0]
rows = data[1:]
for j in range(len(headers)):
tem = []
for i in rows:
tem += [i[j]]
col += [tem]
for i in range(len(headers)):
for x in range(i+1,len(headers)):
correlation = pearsonr(col[i],col[x])[0]
output.append((headers[i],headers[x],float(correlation)))
output.sort(key = lambda x: abs(x[2]))
return output
# problem 5
def my_cluster(data,k):
return KMeans(n_clusters=k, random_state=0,n_init="auto".fit(data))
# Do not change -- for visualization
# def my_plot(data,cluster_object):
# fig, ax = plt.subplots()
# X = np.array(data)
# colors = ["#4EACC5", "#FF9C34"]
# centroid_colors = ['red','blue']
# for i,c in zip(range(2),colors):
# plt.scatter(X[cluster_object.labels_ == i,0],X[cluster_object.labels_ == i,1],color = colors[i])
# cntdx,cntdy = cluster_object.cluster_centers_[i]
# plt.scatter(cntdx,cntdy,color = centroid_colors[i], marker='.')
# plt.text(cntdx-1,cntdy-1,f"{cntdx,cntdy}")
# plt.show()
#TEST CASES
if __name__ == "__main__":
# problem 1
# #number of steps
# n = 100000 #You should change the number of steps to see
# #to see how it affects the plot
# x = np.zeros(n)
# y = np.zeros(n)
# constant = 54
# #fill array with step values
# for i in range(1,n):
# step(x,y,i, constant+i)
# graphit(x,y,n)
# #problem 2
# x = Line(points = ((-2,3),(3,5)))
# y = Line(point_intercept = ((-2,3), 3.8))
# z = Line(function = '0.4x + 3.8')
# a = Line(point_slope = ((-2,3),.4))
# b = Line(function = 'x - 3')
# print(str(x)[1])
# print(x(2),x)
# print(y(2),y)
# print(z(2),z)
# print(a(2),a)
# print(b(2),b)
# M1 = Line(function = 'x + 3')
# N2 = Line(function = '-2x + 12')
# P3 = Line(function = '-2x + 11')
# print(M1 & N2)
# print(M1 & M1)
# print(N2 & P3)
# l1 = Line(function = 'x + 1')
# l2 = Line(function = 'x - 5')
# print(f"{l1} intersect {l2} yields {l1 & l2}")
# #problem 3
# # QUERY 1 Select all the tuples
# print("Query 1")
# print(query1(my_cursor))
# print("List Comprehension: ", data)
# # QUERY 2
# # Select All the tuples where the high temperature is less than 80
# print("\nQuery 2")
# print(query2(my_cursor))
# print("List Comprehension: ", [d for d in data if d[2] < 80 ])
# # QUERY 3
# # Select All the cities where the low temperature is greater than the low of Albuquerque
# print("\nQuery 3")
# print(query3(my_cursor))
# print("List Comprehension: ",[d[0] for d in data if d[3] > [d[3] for d in data if d[0] == 'Alguquerque'][0]])
# # QUERY 4
# # Select the city and temperature with the smallest low temperature
# print("\nQuery 4")
# print(query4(my_cursor))
# print("List Comprehension: ",[(d[0],d[3]) for d in data if d[3] in (sorted(data, key = lambda x:x[3])[0])])
# # QUERY 5
# # Select the city temperature with the largest high temperature
# print("\nQuery 5")
# print(query5(my_cursor))
# print("List Comprehension: ",[(d[0],d[2]) for d in data if d[2] in (sorted(data, key = lambda x:x[2],reverse=True)[0])])
# # QUERY 6
# # Display the average High and Low temperatures
# # You are not allowed to use Avg()
# print("\nQuery 6")
# print(query6(my_cursor))
# print("List Comprehension: ", [(sum([d[2] for d in data])/len(data),sum([d[3] for d in data])/len(data))])
# # QUERY 7
# # Give the counts of cities by their Low temperatures
# print(query7(my_cursor))
# print("List Comprehension: ", sorted([(i,list(map((lambda x: x[3]),data)).count(i)) for i in set(map((lambda x: x[3]),data))]))
# problem 4
data4 = [('A', 'B', 'C', 'D'), (30, 31, 28, 932), (1, 2, 9, 7), (33, 35, 6, 1127), (14, 15, 18, 213), (5, 7, 3, 33), (7, 9, 29, 61), (19, 21, 15, 383), (10, 12, 16, 113)]
print(data4)
print(correlation4(data4))
# problem 5
# data_from_class = [(2,5),(1,5),(1,10), (10,1),(9,2), (12,15),(11,15),(5,5), (6, 6),(4,3)]
# cluster_object = my_cluster(data_from_class,2)
# print(cluster_object.cluster_centers_)
# print(cluster_object.labels_)
# my_plot(data_from_class,cluster_object)
# data for homework
# data5 = [(30, 31, 28, 932), (1, 2, 9, 7), (33, 35, 6, 1127),
# (14, 15, 18, 213), (5, 7, 3, 33), (7, 9, 29, 61),
# (19, 21, 15, 383), (10, 12, 16, 113)]
# datac = replace this with the two least correlated columns from data5
# cluster_object = my_cluster(datac, 2)
# print(cluster_object.cluster_centers_)
# print(cluster_object.labels_)
# my_plot(data,cluster_object)
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