BenBausch

import numpy as np

def k_means(data, n_clusters):
    # choose n_clusters data points randomly for the initialization
    centroids_ids = np.random.choice(len(data), 3, replace=False)
    centroids = [data[i] for i in centroids_ids]

    asignment = [-1 for i in range(len(data))]
    # convergence: no more data point changes class
    no_change = False

BenBausch

# disclaimer: example code not recommended for usage

def covariance_estimation(X):
    mean_features = np.sum(X, axis=0) / X.shape[0]
    X = X - mean_features
    cov = np.dot(X.transpose(), X) / (X.shape[0] - 1)
    return cov
  
def train_linear_model(X, y):
    y_1 = np.where(y == 1, y, 0)

BenBausch

"""
Author: Ben Bausch
"""
import numpy as np


def generateRadnomLayout(bs_num=1, rs_num=2, cs_num=2, ds_num=1, width=30, length=30, agentNum=3):
  """
  inputs:
  :param bs_num: number of base stations