def fuzzy_kmeans(points, k=10, num_iter=10, m=2.0, centers=None):
'''
clustering data points using fuzzy kmeans clustering method.
Args:
points(Expr or DistArray): the input data points matrix.
k(int): the number of clusters.
num_iter(int): the max iterations to run.
m(float): the parameter of fuzzy kmeans.
centers(Expr or DistArray): the initialized centers of each cluster.
'''
points = points.evaluate()
num_dim = points.shape[1]
if centers is None:
centers = expr.rand(k, num_dim)
#labels = expr.zeros((points.shape[0],), dtype=np.int)
for iter in range(num_iter):
centers = centers.glom()
fuzzy = expr.map2(points, 0, fn=kmeans_map2_dist_mapper,
fn_kw={"centers": centers, "m": m},
shape=(points.shape[0], centers.shape[0]))
labels = expr.argmax(fuzzy, axis=1)
new_centers = expr.map2((points, fuzzy), (0, 0), fn=kmeans_map2_center_mapper,
fn_kw={"centers": centers, "m": m},
shape=(centers.shape[0], centers.shape[1]), reducer=np.add)
new_centers /= expr.sum(fuzzy ** m, axis=0)[:, expr.newaxis]
centers = new_centers
return labels
def cholesky(A):
'''
Cholesky matrix decomposition.
Args:
A(Expr): matrix to be decomposed
'''
A = expr.force(A)
n = int(math.sqrt(len(A.tiles)))
tile_size = A.shape[0] / n
for k in range(n):
# A[k,k] = DPOTRF(A[k,k])
diag_ex = get_ex(k, k, tile_size, A.shape)
A = expr.map2(A, ((0, 1), ),
fn=_cholesky_dpotrf_mapper,
shape=A.shape,
update_region=diag_ex)
if k == n - 1: break
# A[l,k] = DTRSM(A[k,k], A[l,k]) l -> [k+1,n)
col_ex = extent.create(((k + 1) * tile_size, k * tile_size),
(n * tile_size, (k + 1) * tile_size), A.shape)
diag_tile = A.force().fetch(diag_ex)
A = expr.map2(A, ((0, 1), ),
fn=_cholesky_dtrsm_mapper,
fn_kw=dict(array=force(A), diag_tile=diag_tile),
shape=A.shape,
update_region=col_ex)
# A[m,m] = DSYRK(A[m,k], A[m,m]) m -> [k+1,n)
# A[l,m] = DGEMM(A[l,k], A[m,k], A[l,m]) m -> [k+1,n) l -> [m+1,n)
col_exs = list([
extent.create((m * tile_size, m * tile_size),
(n * tile_size, (m + 1) * tile_size), A.shape)
for m in range(k + 1, n)
])
dgemm_1 = expr.transpose(A)[(k * tile_size):((k + 1) * tile_size), :]
dgemm_2 = A[:, (k * tile_size):((k + 1) * tile_size)]
A = expr.map2((A, dgemm_1, dgemm_2), ((0, 1), 1, 0),
fn=_cholesky_dsyrk_dgemm_mapper,
fn_kw=dict(array=force(A), k=k),
shape=A.shape,
update_region=col_exs)
# update the right corner to 0
col_exs = list([
extent.create((0, m * tile_size), (m * tile_size, (m + 1) * tile_size),
A.shape) for m in range(1, n)
])
A = expr.map2(A, ((0, 1), ),
fn=_zero_mapper,
shape=A.shape,
update_region=col_exs)
return A
def cholesky(A):
'''
Cholesky matrix decomposition.
Args:
A(Expr): matrix to be decomposed
'''
n = int(math.sqrt(FLAGS.num_workers))
tile_size = A.shape[0] / n
print n, tile_size
for k in range(n):
# A[k,k] = DPOTRF(A[k,k])
diag_ex = get_ex(k, k, tile_size, A.shape)
A = expr.map2(A, ((0, 1), ),
fn=_cholesky_dpotrf_mapper,
shape=A.shape,
update_region=diag_ex)
if k == n - 1: break
# A[l,k] = DTRSM(A[k,k], A[l,k]) l -> [k+1,n)
col_ex = extent.create(((k + 1) * tile_size, k * tile_size),
(n * tile_size, (k + 1) * tile_size), A.shape)
A = expr.map2((A, A[diag_ex.to_slice()]), ((0, 1), None),
fn=_cholesky_dtrsm_mapper,
shape=A.shape,
update_region=col_ex)
# A[m,m] = DSYRK(A[m,k], A[m,m]) m -> [k+1,n)
# A[l,m] = DGEMM(A[l,k], A[m,k], A[l,m]) m -> [k+1,n) l -> [m+1,n)
col_exs = list([
extent.create((m * tile_size, m * tile_size),
(n * tile_size, (m + 1) * tile_size), A.shape)
for m in range(k + 1, n)
])
dgemm = A[:, (k * tile_size):((k + 1) * tile_size)]
A = expr.map2((A, expr.transpose(dgemm), dgemm), ((0, 1), 1, 0),
fn=_cholesky_dsyrk_dgemm_mapper,
shape=A.shape,
update_region=col_exs).optimized()
# update the right corner to 0
col_exs = list([
extent.create((0, m * tile_size), (m * tile_size, (m + 1) * tile_size),
A.shape) for m in range(1, n)
])
A = expr.map2(A, ((0, 1), ),
fn=_zero_mapper,
shape=A.shape,
update_region=col_exs)
return A
def cholesky(A):
'''
Cholesky matrix decomposition.
Args:
A(Expr): matrix to be decomposed
'''
n = int(math.sqrt(FLAGS.num_workers))
tile_size = A.shape[0] / n
print n, tile_size
for k in range(n):
# A[k,k] = DPOTRF(A[k,k])
diag_ex = get_ex(k, k, tile_size, A.shape)
A = expr.map2(A, ((0, 1), ), fn=_cholesky_dpotrf_mapper,
shape=A.shape, update_region=diag_ex)
if k == n - 1: break
# A[l,k] = DTRSM(A[k,k], A[l,k]) l -> [k+1,n)
col_ex = extent.create(((k+1)*tile_size, k*tile_size), (n*tile_size, (k+1)*tile_size), A.shape)
A = expr.map2((A, A[diag_ex.to_slice()]), ((0, 1), None), fn=_cholesky_dtrsm_mapper,
shape=A.shape, update_region=col_ex)
# A[m,m] = DSYRK(A[m,k], A[m,m]) m -> [k+1,n)
# A[l,m] = DGEMM(A[l,k], A[m,k], A[l,m]) m -> [k+1,n) l -> [m+1,n)
col_exs = list([extent.create((m*tile_size, m*tile_size), (n*tile_size, (m+1)*tile_size), A.shape) for m in range(k+1, n)])
dgemm = A[:, (k * tile_size):((k + 1) * tile_size)]
A = expr.map2((A, expr.transpose(dgemm), dgemm), ((0, 1), 1, 0),
fn=_cholesky_dsyrk_dgemm_mapper,
shape=A.shape, update_region=col_exs).optimized()
# update the right corner to 0
col_exs = list([extent.create((0, m*tile_size), (m*tile_size, (m+1)*tile_size), A.shape) for m in range(1, n)])
A = expr.map2(A, ((0, 1), ), fn=_zero_mapper,
shape=A.shape, update_region=col_exs)
return A
def fit(self, X, centers=None, implementation='outer'):
"""Compute k-means clustering.
Parameters
----------
X : spartan matrix, shape=(n_samples, n_features). It should be tiled by rows.
centers : numpy.ndarray. The initial centers. If None, it will be randomly generated.
"""
num_dim = X.shape[1]
num_points = X.shape[0]
labels = expr.zeros((num_points, 1), dtype=np.int)
if implementation == 'map2':
if centers is None:
centers = np.random.rand(self.n_clusters, num_dim)
for i in range(self.n_iter):
labels = expr.map2(X, 0, fn=kmeans_map2_dist_mapper, fn_kw={"centers": centers},
shape=(X.shape[0], ))
counts = expr.map2(labels, 0, fn=kmeans_count_mapper,
fn_kw={'centers_count': self.n_clusters},
shape=(centers.shape[0], ))
new_centers = expr.map2((X, labels), (0, 0), fn=kmeans_center_mapper,
fn_kw={'centers_count': self.n_clusters},
shape=(centers.shape[0], centers.shape[1]))
counts = counts.optimized().glom()
centers = new_centers.optimized().glom()
# If any centroids don't have any points assigined to them.
zcount_indices = (counts == 0).reshape(self.n_clusters)
if np.any(zcount_indices):
# One or more centroids may not have any points assigned to them,
# which results in their position being the zero-vector. We reseed these
# centroids with new random values.
n_points = np.count_nonzero(zcount_indices)
# In order to get rid of dividing by zero.
counts[zcount_indices] = 1
centers[zcount_indices, :] = np.random.randn(n_points, num_dim)
centers = centers / counts.reshape(centers.shape[0], 1)
return centers, labels
elif implementation == 'outer':
if centers is None:
centers = expr.rand(self.n_clusters, num_dim)
for i in range(self.n_iter):
labels = expr.outer((X, centers), (0, None), fn=kmeans_outer_dist_mapper,
shape=(X.shape[0],))
#labels = expr.argmin(distances, axis=1)
counts = expr.map2(labels, 0, fn=kmeans_count_mapper,
fn_kw={'centers_count': self.n_clusters},
shape=(centers.shape[0], ))
new_centers = expr.map2((X, labels), (0, 0), fn=kmeans_center_mapper,
fn_kw={'centers_count': self.n_clusters},
shape=(centers.shape[0], centers.shape[1]))
counts = counts.optimized().glom()
centers = new_centers.optimized().glom()
# If any centroids don't have any points assigined to them.
zcount_indices = (counts == 0).reshape(self.n_clusters)
if np.any(zcount_indices):
# One or more centroids may not have any points assigned to them,
# which results in their position being the zero-vector. We reseed these
# centroids with new random values.
n_points = np.count_nonzero(zcount_indices)
# In order to get rid of dividing by zero.
counts[zcount_indices] = 1
centers[zcount_indices, :] = np.random.randn(n_points, num_dim)
centers = centers / counts.reshape(centers.shape[0], 1)
centers = expr.from_numpy(centers)
return centers, labels
elif implementation == 'broadcast':
if centers is None:
centers = expr.rand(self.n_clusters, num_dim)
for i in range(self.n_iter):
util.log_warn("k_means_ %d %d", i, time.time())
X_broadcast = expr.reshape(X, (X.shape[0], 1, X.shape[1]))
centers_broadcast = expr.reshape(centers, (1, centers.shape[0],
centers.shape[1]))
distances = expr.sum(expr.square(X_broadcast - centers_broadcast), axis=2)
labels = expr.argmin(distances, axis=1)
center_idx = expr.arange((1, centers.shape[0]))
matches = expr.reshape(labels, (labels.shape[0], 1)) == center_idx
matches = matches.astype(np.int64)
counts = expr.sum(matches, axis=0)
centers = expr.sum(X_broadcast * expr.reshape(matches, (matches.shape[0],
matches.shape[1], 1)),
axis=0)
counts = counts.optimized().glom()
centers = centers.optimized().glom()
# If any centroids don't have any points assigined to them.
zcount_indices = (counts == 0).reshape(self.n_clusters)
if np.any(zcount_indices):
# One or more centroids may not have any points assigned to them,
# which results in their position being the zero-vector. We reseed these
# centroids with new random values.
n_points = np.count_nonzero(zcount_indices)
# In order to get rid of dividing by zero.
counts[zcount_indices] = 1
centers[zcount_indices, :] = np.random.randn(n_points, num_dim)
centers = centers / counts.reshape(centers.shape[0], 1)
centers = expr.from_numpy(centers)
return centers, labels
elif implementation == 'shuffle':
if centers is None:
centers = np.random.rand(self.n_clusters, num_dim)
for i in range(self.n_iter):
# Reset them to zero.
new_centers = expr.ndarray((self.n_clusters, num_dim),
reduce_fn=lambda a, b: a + b)
new_counts = expr.ndarray((self.n_clusters, 1), dtype=np.int,
reduce_fn=lambda a, b: a + b)
_ = expr.shuffle(X,
_find_cluster_mapper,
kw={'d_pts': X,
'old_centers': centers,
'new_centers': new_centers,
'new_counts': new_counts,
'labels': labels},
shape_hint=(1,),
cost_hint={hash(labels): {'00': 0,
'01': np.prod(labels.shape)}})
_.force()
new_counts = new_counts.glom()
new_centers = new_centers.glom()
# If any centroids don't have any points assigined to them.
zcount_indices = (new_counts == 0).reshape(self.n_clusters)
if np.any(zcount_indices):
# One or more centroids may not have any points assigned to them,
# which results in their position being the zero-vector. We reseed these
# centroids with new random values.
n_points = np.count_nonzero(zcount_indices)
# In order to get rid of dividing by zero.
new_counts[zcount_indices] = 1
new_centers[zcount_indices, :] = np.random.randn(n_points, num_dim)
new_centers = new_centers / new_counts
centers = new_centers
return centers, labels
def fit(self, X, centers=None, implementation='map2'):
"""Compute k-means clustering.
Parameters
----------
X : spartan matrix, shape=(n_samples, n_features). It should be tiled by rows.
centers : numpy.ndarray. The initial centers. If None, it will be randomly generated.
"""
num_dim = X.shape[1]
num_points = X.shape[0]
labels = expr.zeros((num_points, 1), dtype=np.int)
if implementation == 'map2':
if centers is None:
centers = np.random.rand(self.n_clusters, num_dim)
for i in range(self.n_iter):
labels = expr.map2(X,
0,
fn=kmeans_map2_dist_mapper,
fn_kw={"centers": centers},
shape=(X.shape[0], ))
counts = expr.map2(labels,
0,
fn=kmeans_count_mapper,
fn_kw={'centers_count': self.n_clusters},
shape=(centers.shape[0], ))
new_centers = expr.map2(
(X, labels), (0, 0),
fn=kmeans_center_mapper,
fn_kw={'centers_count': self.n_clusters},
shape=(centers.shape[0], centers.shape[1]))
counts = counts.optimized().glom()
centers = new_centers.optimized().glom()
# If any centroids don't have any points assigined to them.
zcount_indices = (counts == 0).reshape(self.n_clusters)
if np.any(zcount_indices):
# One or more centroids may not have any points assigned to them,
# which results in their position being the zero-vector. We reseed these
# centroids with new random values.
n_points = np.count_nonzero(zcount_indices)
# In order to get rid of dividing by zero.
counts[zcount_indices] = 1
centers[zcount_indices, :] = np.random.randn(
n_points, num_dim)
centers = centers / counts.reshape(centers.shape[0], 1)
return centers, labels
elif implementation == 'outer':
if centers is None:
centers = expr.rand(self.n_clusters, num_dim)
for i in range(self.n_iter):
labels = expr.outer((X, centers), (0, None),
fn=kmeans_outer_dist_mapper,
shape=(X.shape[0], ))
#labels = expr.argmin(distances, axis=1)
counts = expr.map2(labels,
0,
fn=kmeans_count_mapper,
fn_kw={'centers_count': self.n_clusters},
shape=(centers.shape[0], ))
new_centers = expr.map2(
(X, labels), (0, 0),
fn=kmeans_center_mapper,
fn_kw={'centers_count': self.n_clusters},
shape=(centers.shape[0], centers.shape[1]))
counts = counts.optimized().glom()
centers = new_centers.optimized().glom()
# If any centroids don't have any points assigined to them.
zcount_indices = (counts == 0).reshape(self.n_clusters)
if np.any(zcount_indices):
# One or more centroids may not have any points assigned to them,
# which results in their position being the zero-vector. We reseed these
# centroids with new random values.
n_points = np.count_nonzero(zcount_indices)
# In order to get rid of dividing by zero.
counts[zcount_indices] = 1
centers[zcount_indices, :] = np.random.randn(
n_points, num_dim)
centers = centers / counts.reshape(centers.shape[0], 1)
centers = expr.from_numpy(centers)
return centers, labels
elif implementation == 'broadcast':
if centers is None:
centers = expr.rand(self.n_clusters, num_dim)
for i in range(self.n_iter):
util.log_warn("k_means_ %d %d", i, time.time())
X_broadcast = expr.reshape(X, (X.shape[0], 1, X.shape[1]))
centers_broadcast = expr.reshape(
centers, (1, centers.shape[0], centers.shape[1]))
distances = expr.sum(expr.square(X_broadcast -
centers_broadcast),
axis=2)
labels = expr.argmin(distances, axis=1)
center_idx = expr.arange((1, centers.shape[0]))
matches = expr.reshape(labels,
(labels.shape[0], 1)) == center_idx
matches = matches.astype(np.int64)
counts = expr.sum(matches, axis=0)
centers = expr.sum(
X_broadcast *
expr.reshape(matches,
(matches.shape[0], matches.shape[1], 1)),
axis=0)
counts = counts.optimized().glom()
centers = centers.optimized().glom()
# If any centroids don't have any points assigined to them.
zcount_indices = (counts == 0).reshape(self.n_clusters)
if np.any(zcount_indices):
# One or more centroids may not have any points assigned to them,
# which results in their position being the zero-vector. We reseed these
# centroids with new random values.
n_points = np.count_nonzero(zcount_indices)
# In order to get rid of dividing by zero.
counts[zcount_indices] = 1
centers[zcount_indices, :] = np.random.randn(
n_points, num_dim)
centers = centers / counts.reshape(centers.shape[0], 1)
centers = expr.from_numpy(centers)
return centers, labels
elif implementation == 'shuffle':
if centers is None:
centers = np.random.rand(self.n_clusters, num_dim)
for i in range(self.n_iter):
# Reset them to zero.
new_centers = expr.ndarray((self.n_clusters, num_dim),
reduce_fn=lambda a, b: a + b)
new_counts = expr.ndarray((self.n_clusters, 1),
dtype=np.int,
reduce_fn=lambda a, b: a + b)
_ = expr.shuffle(X,
_find_cluster_mapper,
kw={
'd_pts': X,
'old_centers': centers,
'new_centers': new_centers,
'new_counts': new_counts,
'labels': labels
},
shape_hint=(1, ),
cost_hint={
hash(labels): {
'00': 0,
'01': np.prod(labels.shape)
}
})
_.force()
new_counts = new_counts.glom()
new_centers = new_centers.glom()
# If any centroids don't have any points assigined to them.
zcount_indices = (new_counts == 0).reshape(self.n_clusters)
if np.any(zcount_indices):
# One or more centroids may not have any points assigned to them,
# which results in their position being the zero-vector. We reseed these
# centroids with new random values.
n_points = np.count_nonzero(zcount_indices)
# In order to get rid of dividing by zero.
new_counts[zcount_indices] = 1
new_centers[zcount_indices, :] = np.random.randn(
n_points, num_dim)
new_centers = new_centers / new_counts
centers = new_centers
return centers, labels
