test.py

import numpy as np
import pandas as pd
from sklearn.linear_model import LogisticRegression
import math

# print(math.exp(np.zeros((5))))
print(np.exp(np.array([0, 1, 2, 3, 4, 5])))

# n_estimators = 2
# model = np.array([[1, 2, 1, 1, 0, 2], [2, 1, 0, 1, 0, 2]])
vote = np.zeros((6, 3))

for i in range(n_estimators):
    vote[range(6), model[i]] += 0.5 + i
# print(model[0])

# # for i in range(n_estimators):
# #     vote[model[i]] += i + 0.5

# print(vote.max(axis=1) / vote.sum(axis=1))

# n_cols = X.shape[1]
# k = int(X.size * 0.01)  # Number of values to replace (1% of array size)

# # Get the indices of the smallest k values in each column
# smallest_indices = np.argsort(X, axis=0)[1]
# # smallest_indeices = np.argsort(X, axis=0)[:2]

# # smallest_indices = np.array([1, 2, 2, 2])

# print(smallest_indices)

# # Calculate the threshold value as the smallest value among the selected indices
# # threshold = X[smallest_indices, 1]
# threshold = X.values[smallest_indices, np.arange(n_cols)]
# print("threshhold: ", threshold)

# # print(np.where(X <= threshold))

# below_threshold_indices = np.where(X <= threshold)
# print(below_threshold_indices)


# Replace the smallest values in each column with the threshold
# X[smallest_indices] = threshold


# cols = X.shape[1]
# smallest_indices = np.argsort(X, axis=0)[:X.shape[0] * 0.01, np.arange(cols)]
# thresholds = X[smallest_indices[-1, np.arange(cols)]]

# # below_threshold_indices = np.where(X <= thresholds)

# # X[below_threshold_indices] = thresholds[below_threshold_indices[1]]
# print(thresholds)
# print(smallest_indices)