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#! /usr/bin/env python
# David Cournapeau
# Last Change: Wed Nov 05 07:00 PM 2008 J
from __future__ import division, print_function, absolute_import
import os.path
import warnings
import numpy as np
from numpy.testing import assert_array_equal, assert_array_almost_equal, \
TestCase, run_module_suite, assert_raises
from scipy.cluster.vq import kmeans, kmeans2, py_vq, py_vq2, vq, ClusterError
try:
from scipy.cluster import _vq
TESTC = True
except ImportError:
print("== Error while importing _vq, not testing C imp of vq ==")
TESTC = False
#Optional:
# import modules that are located in the same directory as this file.
DATAFILE1 = os.path.join(os.path.dirname(__file__), "data.txt")
# Global data
X = np.array([[3.0, 3], [4, 3], [4, 2],
[9, 2], [5, 1], [6, 2], [9, 4],
[5, 2], [5, 4], [7, 4], [6, 5]])
CODET1 = np.array([[3.0000, 3.0000],
[6.2000, 4.0000],
[5.8000, 1.8000]])
CODET2 = np.array([[11.0/3, 8.0/3],
[6.7500, 4.2500],
[6.2500, 1.7500]])
LABEL1 = np.array([0, 1, 2, 2, 2, 2, 1, 2, 1, 1, 1])
class TestVq(TestCase):
def test_py_vq(self):
initc = np.concatenate(([[X[0]], [X[1]], [X[2]]]))
label1 = py_vq(X, initc)[0]
assert_array_equal(label1, LABEL1)
def test_py_vq2(self):
initc = np.concatenate(([[X[0]], [X[1]], [X[2]]]))
label1 = py_vq2(X, initc)[0]
assert_array_equal(label1, LABEL1)
def test_vq(self):
initc = np.concatenate(([[X[0]], [X[1]], [X[2]]]))
if TESTC:
label1, dist = _vq.vq(X, initc)
assert_array_equal(label1, LABEL1)
tlabel1, tdist = vq(X, initc)
else:
print("== not testing C imp of vq ==")
#def test_py_vq_1d(self):
# """Test special rank 1 vq algo, python implementation."""
# data = X[:, 0]
# initc = data[:3]
# a, b = _py_vq_1d(data, initc)
# ta, tb = py_vq(data[:, np.newaxis], initc[:, np.newaxis])
# assert_array_equal(a, ta)
# assert_array_equal(b, tb)
def test_vq_1d(self):
"""Test special rank 1 vq algo, python implementation."""
data = X[:, 0]
initc = data[:3]
if TESTC:
a, b = _vq.vq(data, initc)
ta, tb = py_vq(data[:, np.newaxis], initc[:, np.newaxis])
assert_array_equal(a, ta)
assert_array_equal(b, tb)
else:
print("== not testing C imp of vq (rank 1) ==")
def test__vq_sametype(self):
if TESTC:
a = np.array([1, 2])
b = a.astype(float)
assert_raises(ValueError, _vq.vq, a, b)
class TestKMean(TestCase):
def test_large_features(self):
# Generate a data set with large values, and run kmeans on it to
# (regression for 1077).
d = 300
n = 100
m1 = np.random.randn(d)
m2 = np.random.randn(d)
x = 10000 * np.random.randn(n, d) - 20000 * m1
y = 10000 * np.random.randn(n, d) + 20000 * m2
data = np.empty((x.shape[0] + y.shape[0], d), np.double)
data[:x.shape[0]] = x
data[x.shape[0]:] = y
kmeans(data, 2)
def test_kmeans_simple(self):
initc = np.concatenate(([[X[0]], [X[1]], [X[2]]]))
code = initc.copy()
code1 = kmeans(X, code, iter=1)[0]
assert_array_almost_equal(code1, CODET2)
def test_kmeans_lost_cluster(self):
# This will cause kmean to have a cluster with no points.
data = np.fromfile(DATAFILE1, sep=", ")
data = data.reshape((200, 2))
initk = np.array([[-1.8127404, -0.67128041],
[2.04621601, 0.07401111],
[-2.31149087,-0.05160469]])
kmeans(data, initk)
with warnings.catch_warnings():
warnings.simplefilter('ignore', UserWarning)
kmeans2(data, initk, missing='warn')
assert_raises(ClusterError, kmeans2, data, initk, missing='raise')
def test_kmeans2_simple(self):
"""Testing simple call to kmeans2 and its results."""
initc = np.concatenate(([[X[0]], [X[1]], [X[2]]]))
code = initc.copy()
code1 = kmeans2(X, code, iter=1)[0]
code2 = kmeans2(X, code, iter=2)[0]
assert_array_almost_equal(code1, CODET1)
assert_array_almost_equal(code2, CODET2)
def test_kmeans2_rank1(self):
"""Testing simple call to kmeans2 with rank 1 data."""
data = np.fromfile(DATAFILE1, sep=", ")
data = data.reshape((200, 2))
data1 = data[:, 0]
data2 = data[:, 1]
initc = data1[:3]
code = initc.copy()
code1 = kmeans2(data1, code, iter=1)[0]
code2 = kmeans2(data1, code, iter=2)[0]
def test_kmeans2_rank1_2(self):
"""Testing simple call to kmeans2 with rank 1 data."""
data = np.fromfile(DATAFILE1, sep=", ")
data = data.reshape((200, 2))
data1 = data[:, 0]
code1 = kmeans2(data1, 2, iter=1)
def test_kmeans2_init(self):
"""Testing that kmeans2 init methods work."""
data = np.fromfile(DATAFILE1, sep=", ")
data = data.reshape((200, 2))
kmeans2(data, 3, minit='points')
kmeans2(data[:, :1], 3, minit='points') # special case (1-D)
# minit='random' can give warnings, filter those
with warnings.catch_warnings():
warnings.filterwarnings('ignore',
message="One of the clusters is empty. Re-run")
kmeans2(data, 3, minit='random')
kmeans2(data[:, :1], 3, minit='random') # special case (1-D)
def test_kmeans2_empty(self):
"""Ticket #505."""
assert_raises(ValueError, kmeans2, [], 2)
def test_kmeans_0k(self):
"""Regression test for #546: fail when k arg is 0."""
assert_raises(ValueError, kmeans, X, 0)
assert_raises(ValueError, kmeans2, X, 0)
assert_raises(ValueError, kmeans2, X, np.array([]))
if __name__ == "__main__":
run_module_suite()
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