def test_predict_with_cosine_distance(self):
points = np.array([[2.5, 3.5], [2, 8], [3, 1], [3, 18], [-2.5, -3.5],
[-2, -8], [-3, -1], [-3, -18]]).astype(np.float32)
true_centers = np.array([
normalize(np.mean(normalize(points)[0:4, :], axis=0,
keepdims=True))[0],
normalize(np.mean(normalize(points)[4:, :], axis=0,
keepdims=True))[0]
])
true_assignments = [0] * 4 + [1] * 4
true_score = len(points) - np.tensordot(normalize(points),
true_centers[true_assignments])
kmeans = KMeans(2,
initial_clusters=kmeans_ops.RANDOM_INIT,
distance_metric=kmeans_ops.COSINE_DISTANCE,
use_mini_batch=self.use_mini_batch,
config=self.config(3))
kmeans.fit(x=points, steps=30, batch_size=8)
centers = normalize(kmeans.clusters())
self.assertAllClose(np.sort(centers, axis=0),
np.sort(true_centers, axis=0),
atol=1e-2)
assignments = kmeans.predict(points, batch_size=8)
self.assertAllClose(centers[assignments],
true_centers[true_assignments],
atol=1e-2)
score = kmeans.score(points, batch_size=8)
self.assertAllClose(score, true_score, atol=1e-2)
def test_predict_with_cosine_distance_and_kmeans_plus_plus(self):
# Most points are concetrated near one center. KMeans++ is likely to find
# the less populated centers.
points = np.array([[2.5, 3.5], [2.5, 3.5], [-2, 3], [-2, 3], [-3, -3],
[-3.1, -3.2], [-2.8, -3.], [-2.9, -3.1], [-3., -3.1],
[-3., -3.1], [-3.2, -3.], [-3., -3.]]).astype(np.float32)
true_centers = np.array(
[normalize(np.mean(normalize(points)[0:2, :], axis=0,
keepdims=True))[0],
normalize(np.mean(normalize(points)[2:4, :], axis=0,
keepdims=True))[0],
normalize(np.mean(normalize(points)[4:, :], axis=0,
keepdims=True))[0]])
true_assignments = [0] * 2 + [1] * 2 + [2] * 8
true_score = len(points) - np.tensordot(normalize(points),
true_centers[true_assignments])
kmeans = KMeans(3,
initial_clusters=kmeans_ops.KMEANS_PLUS_PLUS_INIT,
distance_metric=kmeans_ops.COSINE_DISTANCE,
use_mini_batch=self.use_mini_batch,
config=self.config(3))
kmeans.fit(x=points, steps=30, batch_size=12)
centers = normalize(kmeans.clusters())
self.assertAllClose(sorted(centers.tolist()),
sorted(true_centers.tolist()),
atol=1e-2)
assignments = kmeans.predict(points, batch_size=12)
self.assertAllClose(centers[assignments],
true_centers[true_assignments], atol=1e-2)
score = kmeans.score(points, batch_size=12)
self.assertAllClose(score, true_score, atol=1e-2)
def test_predict_with_cosine_distance_and_kmeans_plus_plus(self):
# Most points are concetrated near one center. KMeans++ is likely to find
# the less populated centers.
points = np.array([[2.5, 3.5], [2.5, 3.5], [-2, 3], [-2, 3], [-3, -3],
[-3.1, -3.2], [-2.8, -3.], [-2.9, -3.1], [-3., -3.1],
[-3., -3.1], [-3.2, -3.], [-3., -3.]], dtype=np.float32)
true_centers = np.array(
[normalize(np.mean(normalize(points)[0:2, :], axis=0,
keepdims=True))[0],
normalize(np.mean(normalize(points)[2:4, :], axis=0,
keepdims=True))[0],
normalize(np.mean(normalize(points)[4:, :], axis=0,
keepdims=True))[0]], dtype=np.float32)
true_assignments = [0] * 2 + [1] * 2 + [2] * 8
true_score = len(points) - np.tensordot(normalize(points),
true_centers[true_assignments])
kmeans = KMeans(3,
initial_clusters=kmeans_ops.KMEANS_PLUS_PLUS_INIT,
distance_metric=kmeans_ops.COSINE_DISTANCE,
use_mini_batch=self.use_mini_batch,
config=self.config(3))
kmeans.fit(x=points, steps=30, batch_size=12)
centers = normalize(kmeans.clusters())
self.assertAllClose(sorted(centers.tolist()),
sorted(true_centers.tolist()),
atol=1e-2)
assignments = kmeans.predict(points, batch_size=12)
self.assertAllClose(centers[assignments],
true_centers[true_assignments], atol=1e-2)
score = kmeans.score(points, batch_size=12)
self.assertAllClose(score, true_score, atol=1e-2)
def test_predict_with_cosine_distance(self):
points = np.array([[2.5, 3.5], [2, 8], [3, 1], [3, 18],
[-2.5, -3.5], [-2, -8], [-3, -1], [-3, -18]]).astype(
np.float32)
true_centers = np.array(
[normalize(np.mean(normalize(points)[0:4, :],
axis=0,
keepdims=True))[0],
normalize(np.mean(normalize(points)[4:, :],
axis=0,
keepdims=True))[0]])
true_assignments = [0] * 4 + [1] * 4
true_score = len(points) - np.tensordot(normalize(points),
true_centers[true_assignments])
kmeans = KMeans(2,
initial_clusters=kmeans_ops.RANDOM_INIT,
distance_metric=kmeans_ops.COSINE_DISTANCE,
use_mini_batch=self.use_mini_batch,
config=self.config(3))
kmeans.fit(x=points, steps=30, batch_size=8)
centers = normalize(kmeans.clusters())
self.assertAllClose(np.sort(centers, axis=0),
np.sort(true_centers, axis=0), atol=1e-2)
assignments = kmeans.predict(points, batch_size=8)
self.assertAllClose(centers[assignments],
true_centers[true_assignments], atol=1e-2)
score = kmeans.score(points, batch_size=8)
self.assertAllClose(score, true_score, atol=1e-2)
true_centers = make_random_centers(num_centers,num_dims)
points, _, scores = make_random_points(true_centers,num_points)
from tensorflow.contrib.factorization.python.ops import kmeans as kmeans_ops
from tensorflow.contrib.factorization.python.ops.kmeans import \
KMeansClustering as KMeans
kmeans = KMeans(num_centers=num_centers,
initial_clusters = kmeans_ops.RANDOM_INIT,
use_mini_batch=False,
config=RunConfig(tf_random_seed=14),
random_seed=12)
kmeans.fit(x=points,steps=10,batch_size=8)
clusters = kmeans.clusters()
kmeans.predict(points,batch_size=128)
kmeans.score(points,batch_size=128)
kmeans.transform(points,batch_size=128)
####################################
#支持向量机
def input_fn():
return{
'example_id':tf.constant(['1','2','3']),
'feature1':tf.constant([[0.0],[1.0],[3.0]]),
'feature2':tf.constant([[1.0],[-1.2],[1.0]]),
},tf.constant([1],[0],[1])
feature1 = tf.contrib.layers.real_valued_column('feature1')
feature2 = tf.contrib.layers.real_valued_column('feature2')
