algorithm/Python
1
0
Fork 0
mirror of https://github.com/TheAlgorithms/Python.git synced 2025-07-05 01:09:40 +08:00
Code Issues Packages Projects Releases Wiki Activity

psf/black code formatting (#1277)

Browse Source
This commit is contained in:
William Zhang
2019-10-05 01:14:13 -04:00
committed by Christian Clauss
parent 07f04a2e55
commit 9eac17a408
291 changed files with 6014 additions and 4571 deletions
Download Patch File Download Diff File Expand all files Collapse all files

99
machine_learning/k_means_clust.py
View File

@ -1,4 +1,4 @@
'''README, Author - Anurag Kumar(mailto:anuragkumarak95@gmail.com)
"""README, Author - Anurag Kumar(mailto:anuragkumarak95@gmail.com)
Requirements:
- sklearn
@ -45,17 +45,18 @@ Usage:
5. Have fun..
'''
"""
from sklearn.metrics import pairwise_distances
import numpy as np
TAG = 'K-MEANS-CLUST/ '
TAG = "K-MEANS-CLUST/ "
def get_initial_centroids(data, k, seed=None):
'''Randomly choose k data points as initial centroids'''
if seed is not None: # useful for obtaining consistent results
"""Randomly choose k data points as initial centroids"""
if seed is not None: # useful for obtaining consistent results
np.random.seed(seed)
n = data.shape[0] # number of data points
n = data.shape[0] # number of data points
# Pick K indices from range [0, N).
rand_indices = np.random.randint(0, n, k)
@ -63,30 +64,33 @@ def get_initial_centroids(data, k, seed=None):
# Keep centroids as dense format, as many entries will be nonzero due to averaging.
# As long as at least one document in a cluster contains a word,
# it will carry a nonzero weight in the TF-IDF vector of the centroid.
centroids = data[rand_indices,:]
centroids = data[rand_indices, :]
return centroids
def centroid_pairwise_dist(X,centroids):
return pairwise_distances(X,centroids,metric='euclidean')
def centroid_pairwise_dist(X, centroids):
return pairwise_distances(X, centroids, metric="euclidean")
def assign_clusters(data, centroids):
# Compute distances between each data point and the set of centroids:
# Fill in the blank (RHS only)
distances_from_centroids = centroid_pairwise_dist(data,centroids)
distances_from_centroids = centroid_pairwise_dist(data, centroids)
# Compute cluster assignments for each data point:
# Fill in the blank (RHS only)
cluster_assignment = np.argmin(distances_from_centroids,axis=1)
cluster_assignment = np.argmin(distances_from_centroids, axis=1)
return cluster_assignment
def revise_centroids(data, k, cluster_assignment):
new_centroids = []
for i in range(k):
# Select all data points that belong to cluster i. Fill in the blank (RHS only)
member_data_points = data[cluster_assignment==i]
member_data_points = data[cluster_assignment == i]
# Compute the mean of the data points. Fill in the blank (RHS only)
centroid = member_data_points.mean(axis=0)
new_centroids.append(centroid)
@ -94,79 +98,102 @@ def revise_centroids(data, k, cluster_assignment):
return new_centroids
def compute_heterogeneity(data, k, centroids, cluster_assignment):
heterogeneity = 0.0
for i in range(k):
# Select all data points that belong to cluster i. Fill in the blank (RHS only)
member_data_points = data[cluster_assignment==i, :]
member_data_points = data[cluster_assignment == i, :]
if member_data_points.shape[0] > 0: # check if i-th cluster is non-empty
if member_data_points.shape[0] > 0: # check if i-th cluster is non-empty
# Compute distances from centroid to data points (RHS only)
distances = pairwise_distances(member_data_points, [centroids[i]], metric='euclidean')
squared_distances = distances**2
distances = pairwise_distances(
member_data_points, [centroids[i]], metric="euclidean"
)
squared_distances = distances ** 2
heterogeneity += np.sum(squared_distances)
return heterogeneity
from matplotlib import pyplot as plt
def plot_heterogeneity(heterogeneity, k):
plt.figure(figsize=(7,4))
plt.figure(figsize=(7, 4))
plt.plot(heterogeneity, linewidth=4)
plt.xlabel('# Iterations')
plt.ylabel('Heterogeneity')
plt.title('Heterogeneity of clustering over time, K={0:d}'.format(k))
plt.rcParams.update({'font.size': 16})
plt.xlabel("# Iterations")
plt.ylabel("Heterogeneity")
plt.title("Heterogeneity of clustering over time, K={0:d}".format(k))
plt.rcParams.update({"font.size": 16})
plt.show()
def kmeans(data, k, initial_centroids, maxiter=500, record_heterogeneity=None, verbose=False):
'''This function runs k-means on given data and initial set of centroids.
def kmeans(
data, k, initial_centroids, maxiter=500, record_heterogeneity=None, verbose=False
):
"""This function runs k-means on given data and initial set of centroids.
maxiter: maximum number of iterations to run.(default=500)
record_heterogeneity: (optional) a list, to store the history of heterogeneity as function of iterations
if None, do not store the history.
verbose: if True, print how many data points changed their cluster labels in each iteration'''
verbose: if True, print how many data points changed their cluster labels in each iteration"""
centroids = initial_centroids[:]
prev_cluster_assignment = None
for itr in range(maxiter):
if verbose:
print(itr, end='')
print(itr, end="")
# 1. Make cluster assignments using nearest centroids
cluster_assignment = assign_clusters(data,centroids)
cluster_assignment = assign_clusters(data, centroids)
# 2. Compute a new centroid for each of the k clusters, averaging all data points assigned to that cluster.
centroids = revise_centroids(data,k, cluster_assignment)
centroids = revise_centroids(data, k, cluster_assignment)
# Check for convergence: if none of the assignments changed, stop
if prev_cluster_assignment is not None and \
(prev_cluster_assignment==cluster_assignment).all():
if (
prev_cluster_assignment is not None
and (prev_cluster_assignment == cluster_assignment).all()
):
break
# Print number of new assignments
if prev_cluster_assignment is not None:
num_changed = np.sum(prev_cluster_assignment!=cluster_assignment)
num_changed = np.sum(prev_cluster_assignment != cluster_assignment)
if verbose:
print(' {0:5d} elements changed their cluster assignment.'.format(num_changed))
print(
" {0:5d} elements changed their cluster assignment.".format(
num_changed
)
)
# Record heterogeneity convergence metric
if record_heterogeneity is not None:
# YOUR CODE HERE
score = compute_heterogeneity(data,k,centroids,cluster_assignment)
score = compute_heterogeneity(data, k, centroids, cluster_assignment)
record_heterogeneity.append(score)
prev_cluster_assignment = cluster_assignment[:]
return centroids, cluster_assignment
# Mock test below
if False: # change to true to run this test case.
if False: # change to true to run this test case.
import sklearn.datasets as ds
dataset = ds.load_iris()
k = 3
heterogeneity = []
initial_centroids = get_initial_centroids(dataset['data'], k, seed=0)
centroids, cluster_assignment = kmeans(dataset['data'], k, initial_centroids, maxiter=400,
record_heterogeneity=heterogeneity, verbose=True)
initial_centroids = get_initial_centroids(dataset["data"], k, seed=0)
centroids, cluster_assignment = kmeans(
dataset["data"],
k,
initial_centroids,
maxiter=400,
record_heterogeneity=heterogeneity,
verbose=True,
)
plot_heterogeneity(heterogeneity, k)