def test_transform_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]])
true_centers = [
normalize(np.mean(normalize(points)[4:, :], axis=0,
keepdims=True))[0],
normalize(np.mean(normalize(points)[0:4, :], axis=0,
keepdims=True))[0]
]
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)
true_transform = 1 - cosine_similarity(points, centers)
transform = kmeans.transform(points, batch_size=8)
self.assertAllClose(transform, true_transform, atol=1e-3)
def test_transform_with_cosine_distance(self):
points = np.array(
[[2.5, 0.1], [2, 0.2], [3, 0.1], [4, 0.2],
[0.1, 2.5], [0.2, 2], [0.1, 3], [0.2, 4]], dtype=np.float32)
true_centers = [normalize(np.mean(normalize(points)[4:, :], axis=0,
keepdims=True))[0],
normalize(np.mean(normalize(points)[0:4, :], axis=0,
keepdims=True))[0]]
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(5))
kmeans.fit(x=points, steps=50, batch_size=8)
centers = normalize(kmeans.clusters())
self.assertAllClose(np.sort(centers, axis=0),
np.sort(true_centers, axis=0),
atol=1e-2)
true_transform = 1 - cosine_similarity(points, centers)
transform = kmeans.transform(points, batch_size=8)
self.assertAllClose(transform, true_transform, atol=1e-3)
def test_transform_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]])
true_centers = [normalize(np.mean(normalize(points)[4:, :], axis=0,
keepdims=True))[0],
normalize(np.mean(normalize(points)[0:4, :], axis=0,
keepdims=True))[0]]
kmeans = KMeans(2,
initial_clusters=kmeans_ops.RANDOM_INIT,
distance_metric=kmeans_ops.COSINE_DISTANCE,
use_mini_batch=self.use_mini_batch,
batch_size=8,
continue_training=True,
config=run_config.RunConfig(tf_random_seed=3))
kmeans.fit(x=points, steps=30)
centers = normalize(kmeans.clusters())
self.assertAllClose(np.sort(centers, axis=0),
np.sort(true_centers, axis=0),
atol=1e-2)
true_transform = 1 - cosine_similarity(points, centers)
transform = kmeans.transform(points)
self.assertAllClose(transform, true_transform, atol=1e-3)
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')
svm_classifier = tf.contrib.learn.SVM(feature_columns=[feature1,feature2],
