def main():
k = 3
X = [[random.randint(0,20),random.randint(0,20)] for i in range(30)] \
+ [[random.randint(40,60), random.randint(40,60)] for i in range(30)] \
+ [[random.randint(80, 100), random.randint(80, 100)] for i in range(30)]
print(f"Cluster points:{X}")
kmeans = KMeans(n_cluster=k, tol=3e-4)
centroids = kmeans.fit(X)
prediction = kmeans.predict([[0.0,0.0],[50.0,40.0],[100.0,100.0]])
print(f"KMeans centroids: {centroids}")
print(f"KMeans predict for [0,0],[50,40],[100,100]]: {prediction}")
colors = ['r', 'g', 'b']
for i in range(k):
plt.scatter([x[0] for x in X], [x[1] for x in X], s=7, c=colors[i])
plt.scatter([x[0] for x in centroids], [x[1] for x in centroids], marker='*', s=200, c='black')
plt.show()
from clustering import KMeans, GaussianMixture
from constants import *
utils = Utils()
prep = Preprocessor()
nlp = NLP()
# Only uncomment if data crawling is needed again. Runs Julia script.
# utils.crawl_data("news_crawler.jl")
# Only uncomment if pre-processing raw data again, e.g. after crawling new data. Saves processed documents in /data/raw.
# prep.process_raw_data(STOPS, "متن", ECONOMICS, POLITICS, SPORTS, CULTURE)
# Creates tf-idf vectors from documents. Change to False if changing other arguments.
doc_vectors = nlp.doc_to_vec(250, 200, True)
err_msg = "Error occurred! Check tf_idf vectors before continuing."
k_means = KMeans(4, 15)
if doc_vectors is not None:
# k_means.fit(doc_vectors)
k_means.predict(TEST)
else:
print(err_msg)
mix_model = GaussianMixture(4, 20)
if doc_vectors is not None:
# mix_model.fit(doc_vectors, 30)
mix_model.predict(TEST)
else:
print(err_msg)
def kmeans_area_example():
from time import time
import numpy as np
import matplotlib.pyplot as plt
from sklearn import metrics
from sklearn.cluster import KMeans
from sklearn.datasets import load_digits
from sklearn.decomposition import PCA
from sklearn.preprocessing import scale
np.random.seed(42)
digits = load_digits()
data = scale(digits.data)
n_samples, n_features = data.shape
n_digits = len(np.unique(digits.target))
labels = digits.target
sample_size = 300
print("n_digits: %d, \t n_samples %d, \t n_features %d" %
(n_digits, n_samples, n_features))
print(79 * '_')
print(
'% 9s' % 'init'
' time inertia h**o compl v-meas ARI AMI silhouette')
def bench_k_means(estimator, name, data):
t0 = time()
estimator.fit(data)
print('% 9s %.2fs %i %.3f %.3f %.3f %.3f %.3f %.3f' %
(name, (time() - t0), estimator.inertia_,
metrics.homogeneity_score(labels, estimator.labels_),
metrics.completeness_score(labels, estimator.labels_),
metrics.v_measure_score(labels, estimator.labels_),
metrics.adjusted_rand_score(labels, estimator.labels_),
metrics.adjusted_mutual_info_score(labels, estimator.labels_),
metrics.silhouette_score(data,
estimator.labels_,
metric='euclidean',
sample_size=sample_size)))
bench_k_means(KMeans(init='k-means++', n_clusters=n_digits, n_init=10),
name="k-means++",
data=data)
bench_k_means(KMeans(init='random', n_clusters=n_digits, n_init=10),
name="random",
data=data)
# in this case the seeding of the centers is deterministic, hence we run the
# kmeans algorithm only once with n_init=1
pca = PCA(n_components=n_digits).fit(data)
bench_k_means(KMeans(init=pca.components_, n_clusters=n_digits, n_init=1),
name="PCA-based",
data=data)
print(79 * '_')
reduced_data = PCA(n_components=2).fit_transform(data)
kmeans = KMeans(init='k-means++', n_clusters=n_digits, n_init=10)
kmeans.fit(reduced_data)
# Step size of the mesh. Decrease to increase the quality of the VQ.
h = .02 # point in the mesh [x_min, x_max]x[y_min, y_max].
# Plot the decision boundary. For that, we will assign a color to each
x_min, x_max = reduced_data[:, 0].min() - 1, reduced_data[:, 0].max() + 1
y_min, y_max = reduced_data[:, 1].min() - 1, reduced_data[:, 1].max() + 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
np.arange(y_min, y_max, h))
# Obtain labels for each point in mesh. Use last trained model.
Z = kmeans.predict(np.c_[xx.ravel(), yy.ravel()])
# print Z
# Put the result into a color plot
Z = Z.reshape(xx.shape)
print Z[0]
plt.figure(1)
plt.clf()
plt.imshow(
Z,
#interpolation='nearest',
extent=(xx.min(), xx.max(), yy.min(), yy.max()),
#cmap=plt.cm.Paired,
#aspect='auto', origin='lower'
)
plt.plot(reduced_data[:, 0], reduced_data[:, 1], 'k.', markersize=2)
# Plot the centroids as a white X
centroids = kmeans.cluster_centers_
plt.scatter(centroids[:, 0],
centroids[:, 1],
marker='x',
s=169,
linewidths=3,
color='w',
zorder=10)
plt.title('K-means clustering on the digits dataset (PCA-reduced data)\n'
'Centroids are marked with white cross')
plt.xlim(x_min, x_max)
plt.ylim(y_min, y_max)
plt.xticks(())
plt.yticks(())
plt.show()
