arikaa

import numpy as np

def logistic_regression(data, label, iteration, learning_rate):
    '''
    Logistic regression classifier
    
    Arguments
    data: This is the training data with shape (n, d), which corresponds to n samples and each sample has d features.
    label: This is the training data's label with shape (n,1). The 1 corresponds to the correct classification of the data.
    iteration: The number of times to iterate.

arikaa

def thirdorder(data):
	'''
  3rd order polynomial transform of data
  Arguments
  data: The input data is assumed to have shape (:, 3). The first dimension represents
        the total samples and the second dimension represents the total features.
  Returns
  result: A numpy array format of the new data with shape (:,10), where the feature
          numbers are extended.
	'''

arikaa

import numpy as np

def perceptron(data, label, iteration, y):
	'''
	The perceptron classifier

	Arguments
	data: This is the training data with a certain shape (x, y), meaning there are x samples and each sample has y features.
	label: This is the training data's label with shape (x, 1). The 1 corresponds to the correct classification of the data.
	iteration: This is the number of times the function will iterate.