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Merge branch 'main' of ssh://gitea.snsd0805.com:10805/snsd0805/NTU_HTML

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This commit is contained in:
snsd0805 2023-11-14 02:26:36 +08:00
commit 2e5fcb284f
Signed by: snsd0805
GPG Key ID: 569349933C77A854
4 changed files with 19 additions and 19 deletions
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8
hw3/hw3_10.py
View File

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

8
hw3/hw3_11.py
View File

@ -77,10 +77,10 @@ if __name__ == '__main__':
print()
linear_regression_errors = sorted(linear_regression_errors)
logistic_regression_errors = sorted(logistic_regression_errors)
linear_regression_median = linear_regression_errors[63] + linear_regression_errors[64]
logistic_regression_median = logistic_regression_errors[63] + logistic_regression_errors[64]
sorted_linear_regression_errors = sorted(linear_regression_errors)
sorted_logistic_regression_errors = sorted(logistic_regression_errors)
linear_regression_median = sorted_linear_regression_errors[63] + sorted_linear_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.xlabel("linear regression error")

9
hw3/hw3_12.py
View File

@ -90,11 +90,10 @@ if __name__ == '__main__':
print(times, error)
print()
linear_regression_errors = sorted(linear_regression_errors)
logistic_regression_errors = sorted(logistic_regression_errors)
linear_regression_median = linear_regression_errors[63] + linear_regression_errors[64]
logistic_regression_median = logistic_regression_errors[63] + logistic_regression_errors[64]
sorted_linear_regression_errors = sorted(linear_regression_errors)
sorted_logistic_regression_errors = sorted(logistic_regression_errors)
linear_regression_median = sorted_linear_regression_errors[63] + sorted_linear_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.xlabel("linear regression error")

5
hw3/hw3_9.py
View File

@ -2,7 +2,6 @@ import numpy as np
import matplotlib.pyplot as plt
import time
def generate_data(N):
y = np.random.choice([1, -1], N)
@ -42,8 +41,8 @@ if __name__ == '__main__':
errors.append(error)
print(times, error)
errors = sorted(errors)
median = ( errors[63] + errors[64] ) / 2
sorted_errors = sorted(errors)
median = ( sorted_errors[63] + sorted_errors[64] ) / 2
plt.hist(errors, bins=10)
plt.xlabel("Ein")