def run_clustering(algorithm):
"""Run all datasets"""
_run_dataset('HART', testloader.load_hartigan(), 3, algorithm)
_run_dataset('IRIS', testloader.load_iris(), 3, algorithm)
_run_dataset('SOYS', testloader.load_soy_small(), 4, algorithm)
_run_dataset('WINE', testloader.load_wine(), 3, algorithm)
_run_dataset('WBCO', testloader.load_wbco(), 2, algorithm)
def test_bigger(self):
"""Try an actual dataset"""
dataset = testloader.load_iris()
matrix = self._proc.process(dataset.data)
ranges = matrix.max(axis=0) - matrix.min(axis=0)
expected = np.array([1., 1., 1., 1.])
np.testing.assert_equal(ranges, expected)
def test_with_iris(self):
"""Crude integration-style test until I can break it down a little.
Does not trigger the merge stage.
"""
dataset = testloader.load_iris()
seeds = ccia.generate(dataset.data, 3)
# As emitted by the Java
expected = np.array([[5.006, 3.428, 1.462, 0.246],
[6.85384615, 3.07692308, 5.71538462, 2.05384615],
[5.88360656, 2.74098361, 4.38852459, 1.43442623]])
np.testing.assert_array_almost_equal(seeds, expected)
def test_against_iris(self):
"""Test run against Iris dataset, as used in the paper"""
dataset = testloader.load_iris()
data = dataset.data
target = dataset.target
num_clusters = 3
centroids = yuan.generate(data, num_clusters)
# sanity check shape
self.assertEqual(centroids.shape, (num_clusters, 4))
# run kmeans
est = KMeans(n_clusters=num_clusters, init=centroids, n_init=1)
est.fit(data)
score = accuracy.score(target, est.labels_)
# Claimed in paper, though quite frankly random will get this a lot
self.assertAlmostEqual(0.886667, score, places=6)
"""Temp bootstrap file to run KM++"""
import pathhack
from sklearn.cluster import KMeans
from datasets import testloader
from initialisations import kmeansplusplus as alg
dataset = testloader.load_iris()
num_clusters = 3
# dataset = testloader.load_hartigan()
# num_clusters = 3
# dataset = testloader.load_soy_small()
# num_clusters = 4
data = dataset.data
centroids = alg.generate(data, num_clusters)
print(centroids)
est = KMeans(n_clusters=num_clusters, init=centroids, n_init=1)
est.fit(dataset.data)
print("Final centres:\n", est.cluster_centers_)
def _get_test_data(self):
"""Fetch some data to test with"""
dataset = testloader.load_iris()
return dataset.data
def test_code_runs(self):
"""This needs more, but at least prove it runs"""
dataset = testloader.load_iris()
centroids = onoda.generate(dataset.data, 3)
self.assertEqual((3, 4), centroids.shape)
def test_code_runs(self):
"""At least prove it runs"""
dataset = testloader.load_iris()
centroids = bfinit.generate(dataset.data, 3)
self.assertEqual((3, 4), centroids.shape)
