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fix: plot unsorted data & mistake 0/1 error

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Ting-Jun Wang 2023-11-01 04:21:03 +08:00
parent b3ddd2d11e
commit 0c07cd70e1
Signed by: snsd0805
GPG Key ID: D175E969960C4B16
4 changed files with 19 additions and 19 deletions
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10
hw3/hw3_10.py
View File

@ -22,7 +22,7 @@ def generate_data(N):
return x, y return x, y
def average_square_error(y, y_hat): def average_square_error(y, y_hat):
error = (y==y_hat) error = (y!=y_hat)
return error.sum()/error.shape[0] return error.sum()/error.shape[0]
if __name__ == '__main__': if __name__ == '__main__':
@ -42,10 +42,12 @@ if __name__ == '__main__':
errors.append(error) errors.append(error)
print(times, error) print(times, error)
errors = sorted(errors) sorted_errors = sorted(errors)
median = ( errors[63] + errors[64] ) / 2 median = ( sorted_errors[63] + sorted_errors[64] ) / 2
plt.hist(errors, bins=10) plt.hist(errors, bins=10)
plt.xlabel("Ein") plt.xlabel("Ein")
plt.title("median: {}".format(median)) plt.title("median: {}".format(median))
plt.savefig("10.png") plt.savefig("10.png")

10
hw3/hw3_11.py
View File

@ -77,13 +77,13 @@ if __name__ == '__main__':
print() print()
linear_regression_errors = sorted(linear_regression_errors) sorted_linear_regression_errors = sorted(linear_regression_errors)
logistic_regression_errors = sorted(logistic_regression_errors) sorted_logistic_regression_errors = sorted(logistic_regression_errors)
linear_regression_median = linear_regression_errors[63] + linear_regression_errors[64] linear_regression_median = sorted_linear_regression_errors[63] + sorted_linear_regression_errors[64]
logistic_regression_median = logistic_regression_errors[63] + logistic_regression_errors[64] logistic_regression_median = sorted_logistic_regression_errors[63] + sorted_logistic_regression_errors[64]
plt.scatter(linear_regression_errors, logistic_regression_errors) plt.scatter(linear_regression_errors, logistic_regression_errors)
plt.xlabel("linear regression error") plt.xlabel("linear regression error")
plt.xlabel("logistic regression error") plt.xlabel("logistic regression error")
plt.title("linear regression: {}\nlogistic regression: {}".format(linear_regression_median, logistic_regression_median)) plt.title("linear regression: {}\nlogistic regression: {}".format(linear_regression_median, logistic_regression_median))
plt.savefig("11.png") plt.savefig("11.png")

11
hw3/hw3_12.py
View File

@ -90,14 +90,13 @@ if __name__ == '__main__':
print(times, error) print(times, error)
print() print()
sorted_linear_regression_errors = sorted(linear_regression_errors)
linear_regression_errors = sorted(linear_regression_errors) sorted_logistic_regression_errors = sorted(logistic_regression_errors)
logistic_regression_errors = sorted(logistic_regression_errors) linear_regression_median = sorted_linear_regression_errors[63] + sorted_linear_regression_errors[64]
linear_regression_median = linear_regression_errors[63] + linear_regression_errors[64] logistic_regression_median = sorted_logistic_regression_errors[63] + sorted_logistic_regression_errors[64]
logistic_regression_median = logistic_regression_errors[63] + logistic_regression_errors[64]
plt.scatter(linear_regression_errors, logistic_regression_errors) plt.scatter(linear_regression_errors, logistic_regression_errors)
plt.xlabel("linear regression error") plt.xlabel("linear regression error")
plt.xlabel("logistic regression error") plt.xlabel("logistic regression error")
plt.title("linear regression: {}\nlogistic regression: {}".format(linear_regression_median, logistic_regression_median)) plt.title("linear regression: {}\nlogistic regression: {}".format(linear_regression_median, logistic_regression_median))
plt.savefig("12.png") plt.savefig("12.png")

7
hw3/hw3_9.py
View File

@ -2,7 +2,6 @@ import numpy as np
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
import time import time
def generate_data(N): def generate_data(N):
y = np.random.choice([1, -1], N) y = np.random.choice([1, -1], N)
@ -42,10 +41,10 @@ if __name__ == '__main__':
errors.append(error) errors.append(error)
print(times, error) print(times, error)
errors = sorted(errors) sorted_errors = sorted(errors)
median = ( errors[63] + errors[64] ) / 2 median = ( sorted_errors[63] + sorted_errors[64] ) / 2
plt.hist(errors, bins=10) plt.hist(errors, bins=10)
plt.xlabel("Ein") plt.xlabel("Ein")
plt.title("median: {}".format(median)) plt.title("median: {}".format(median))
plt.savefig("9.png") plt.savefig("9.png")