#!/usr/bin/env python # coding: utf-8 # In[1]: import numpy as np from bokeh.models import ColumnDataSource, Button, Select, Div from bokeh.sampledata.iris import flowers from bokeh.plotting import figure, curdoc, show from bokeh.layouts import column, row # In[2]: # read and store the dataset data = flowers.copy(deep=True) data = data.drop(['species'], axis=1) # In[194]: dist_matrix = np.empty((m, k)) for i in range(m): dist = np.linalg.norm(pca_data[i, :] - initial_medoids, ord=1, axis=1) dist_matrix[i, :] = dist dist_another = np.repeat(np.sum(np.abs(pca_data - initial_medoids), axis=-1),3) dist_matrix_another = dist_another.reshape((m,k)) # In[6]: #k-medoid algorithm using given medoids m = len(data) #Dimension Reduction from sklearn.decomposition import PCA pca_components = PCA(n_components=3) pca_data = pca_components.fit_transform(data) #initialize the given medoids medoids = [24, 74, 124] initial_medoids = np.array([[24,74,124]])
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