def cgit(A, x):
'''
CGIT Conjugate Gradient iteration
z = cgit(A, x) generates approximate solution to A*z = x.
Args:
A(Expr): matrix to be processed.
x(Expr): the input vector.
'''
z = expr.zeros(x.shape, tile_hint=(A.tile_shape()[1], 1))
r = x
rho = expr.sum(r * r).glom()
#util.log_warn('rho:%s', rho)
p = r
for i in xrange(15):
q = expr.dot(A, p, tile_hint=(A.tile_shape()[1], 1))
alpha = rho / expr.sum(p * q).glom()
#util.log_warn('alpha:%s', alpha)
z = z + p * alpha
rho0 = rho
r = r - q * alpha
rho = expr.sum(r * r).glom()
beta = rho / rho0
#util.log_warn('beta:%s', beta)
p = r + p * beta
return z
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 = expr.force(points)
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 = expr.as_array(centers)
points_broadcast = expr.reshape(points, (points.shape[0], 1, points.shape[1]))
centers_broadcast = expr.reshape(centers, (1, centers.shape[0], centers.shape[1]))
distances = expr.sum(expr.square(points_broadcast - centers_broadcast), axis=2)
# This is used to avoid dividing zero
distances = distances + 0.00000000001
util.log_info('distances shape %s' % str(distances.shape))
distances_broadcast = expr.reshape(distances, (distances.shape[0], 1,
distances.shape[1]))
distances_broadcast2 = expr.reshape(distances, (distances.shape[0],
distances.shape[1], 1))
prob = 1.0 / expr.sum(expr.power(distances_broadcast / distances_broadcast2,
2.0 / (m - 1)), axis=2)
prob.force()
counts = expr.sum(prob, axis=0)
counts = expr.reshape(counts, (counts.shape[0], 1))
labels = expr.argmax(prob, axis=1)
centers = expr.sum(expr.reshape(points, (points.shape[0], 1, points.shape[1])) *
expr.reshape(prob, (prob.shape[0], prob.shape[1], 1)),
axis=0)
# We assume that the size of centers are relative small that can be handled
# on the master.
counts = counts.glom()
centers = centers.glom()
# If any centroids don't have any points assigned to them.
zcount_indices = (counts == 0).reshape(k)
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
# and set their counts to 1 in order to get rid of dividing by zero.
counts[zcount_indices, :] = 1
centers[zcount_indices, :] = np.random.rand(np.count_nonzero(zcount_indices),
num_dim)
centers = centers / counts
return labels
def fit(self, X, centers=None):
"""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 centers is None:
centers = expr.from_numpy(np.random.rand(self.n_clusters, num_dim))
for i in range(self.n_iter):
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
'''
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 _step(): yp = expr.dot(x, w) Assert.all_eq(yp.shape, y.shape) diff = x * (yp - y) grad = expr.sum(diff, axis=0).glom().reshape((N_DIM, 1)) wprime = w - grad * 1e-6 expr.force(wprime)
def kneighbors(self, X, n_neighbors=None):
"""Finds the K-neighbors of a point.
Returns distance
Parameters
----------
X : array-like, last dimension same as that of fit data
The new point.
n_neighbors : int
Number of neighbors to get (default is the value
passed to the constructor).
Returns
-------
dist : array
Array representing the lengths to point, only present if
return_distance=True
ind : array
Indices of the nearest points in the population matrix.
"""
if n_neighbors is not None:
self.n_neighbors = n_neighbors
if isinstance(X, np.ndarray):
X = expr.from_numpy(X)
if self.algorithm in ('auto', 'brute'):
X_broadcast = expr.reshape(X, (X.shape[0], 1, X.shape[1]))
fit_X_broadcast = expr.reshape(self.X, (1, self.X.shape[0], self.X.shape[1]))
distances = expr.sum((X_broadcast - fit_X_broadcast) ** 2, axis=2)
neigh_ind = expr.argsort(distances, axis=1)
neigh_ind = neigh_ind[:, :n_neighbors].optimized().glom()
neigh_dist = expr.sort(distances, axis=1)
neigh_dist = expr.sqrt(neigh_dist[:, :n_neighbors]).optimized().glom()
return neigh_dist, neigh_ind
else:
results = self.X.foreach_tile(mapper_fn=_knn_mapper,
kw={'X': self.X, 'Q': X,
'n_neighbors': self.n_neighbors,
'algorithm': self.algorithm})
dist = None
ind = None
""" Get the KNN candidates for each tile of X, then find out the real KNN """
for k, v in results.iteritems():
if dist is None:
dist = v[0]
ind = v[1]
else:
dist = np.concatenate((dist, v[0]), axis=1)
ind = np.concatenate((ind, v[1]), axis=1)
mask = np.argsort(dist, axis=1)[:, :self.n_neighbors]
new_dist = np.array([dist[i][mask[i]] for i, r in enumerate(dist)])
new_ind = np.array([ind[i][mask[i]] for i, r in enumerate(ind)])
return new_dist, new_ind
def fit(data, labels, label_size, alpha=1.0):
'''
Train standard naive bayes model.
Args:
data(Expr): documents to be trained.
labels(Expr): the correct labels of the training data.
label_size(int): the number of different labels.
alpha(float): alpha parameter of naive bayes model.
'''
labels = expr.force(labels)
# calc document freq
df = expr.reduce(data,
axis=0,
dtype_fn=lambda input: input.dtype,
local_reduce_fn=lambda ex, data, axis: (data > 0).sum(axis),
accumulate_fn=np.add,
tile_hint=(data.shape[1],))
idf = expr.log(data.shape[0] * 1.0 / (df + 1)) + 1
# Normalized Frequency for a feature in a document is calculated by dividing the feature frequency
# by the root mean square of features frequencies in that document
square_sum = expr.reduce(data,
axis=1,
dtype_fn=lambda input: input.dtype,
local_reduce_fn=lambda ex, data, axis: np.square(data).sum(axis),
accumulate_fn=np.add,
tile_hint=(data.shape[0],))
rms = expr.sqrt(square_sum * 1.0 / data.shape[1])
# calculate weight normalized Tf-Idf
data = data / rms.reshape((data.shape[0], 1)) * idf.reshape((1, data.shape[1]))
# add up all the feature vectors with the same labels
sum_instance_by_label = expr.ndarray((label_size, data.shape[1]),
dtype=np.float64,
reduce_fn=np.add,
tile_hint=(label_size / len(labels.tiles), data.shape[1]))
sum_instance_by_label = expr.shuffle(data,
_sum_instance_by_label_mapper,
target=sum_instance_by_label,
kw={'labels': labels, 'label_size': label_size})
# sum up all the weights for each label from the previous step
weights_per_label = expr.sum(sum_instance_by_label, axis=1, tile_hint=(label_size,))
# generate naive bayes per_label_and_feature weights
weights_per_label_and_feature = expr.shuffle(sum_instance_by_label,
_naive_bayes_mapper,
kw={'weights_per_label': weights_per_label,
'alpha':alpha})
return {'scores_per_label_and_feature': weights_per_label_and_feature.force(),
'scores_per_label': weights_per_label.force(),
}
def move(galaxy, dt): '''Move the bodies. First find forces and change velocity and then move positions. ''' # `.reshape(add_tuple(a, 1))` is the spartan way of doing # `ndarray[:, np.newaxis]` in numpy. While syntactically different, both # add a dimension of length 1 after the other dimensions. # e.g. (5, 5) becomes (5, 5, 1) # Calculate all distances component wise (with sign). dx_new = galaxy['x'].reshape(add_tuple(galaxy['x'].shape, [1])) dy_new = galaxy['y'].reshape(add_tuple(galaxy['y'].shape, [1])) dz_new = galaxy['z'].reshape(add_tuple(galaxy['z'].shape, [1])) dx = (galaxy['x'] - dx_new) * -1 dy = (galaxy['y'] - dy_new) * -1 dz = (galaxy['z'] - dz_new) * -1 # Euclidean distances (all bodies). r = sqrt(dx**2 + dy**2 + dz**2) r = set_diagonal(r, 1.0) # Prevent collision. mask = r < 1.0 #r = r * ~mask + 1.0 * mask r = spartan.map((r, mask), lambda x, m: x * ~m + 1.0 * m) m = galaxy['m'].reshape(add_tuple(galaxy['m'].shape, [1])) # Calculate the acceleration component wise. fx = G*m*dx / r**3 fy = G*m*dy / r**3 fz = G*m*dz / r**3 # Set the force (acceleration) a body exerts on itself to zero. fx = set_diagonal(fx, 0.0) fy = set_diagonal(fy, 0.0) fz = set_diagonal(fz, 0.0) galaxy['vx'] += dt*expr.sum(fx, axis=0) galaxy['vy'] += dt*expr.sum(fy, axis=0) galaxy['vz'] += dt*expr.sum(fz, axis=0) galaxy['x'] += dt*galaxy['vx'] galaxy['y'] += dt*galaxy['vy'] galaxy['z'] += dt*galaxy['vz']
def train(self):
self.x = expr.eager(self.x)
self.y = expr.eager(self.y)
for i in range(self.iterations):
diff = self.update()
grad = expr.sum(diff, axis=0, tile_hint=[self.N_DIM]).glom().reshape((self.N_DIM, 1))
self.w = self.w - grad * self.alpha
return self.w
def fn2(): a = expr.ones((N, N)) b = expr.ones((N, N/2)) g = expr.dot(a, b) + expr.dot(expr.sum(a, axis=1).reshape((1, N)), b) t1 = time.time() g_opt = g.optimized() #g_opt.force() t2 = time.time() print t2 - t1 print g_opt
def fit(data, labels, label_size, alpha=1.0):
'''
Train standard naive bayes model.
Args:
data(Expr): documents to be trained.
labels(Expr): the correct labels of the training data.
label_size(int): the number of different labels.
alpha(float): alpha parameter of naive bayes model.
'''
# calc document freq
df = expr.reduce(data,
axis=0,
dtype_fn=lambda input: input.dtype,
local_reduce_fn=lambda ex, data, axis: (data > 0).sum(axis),
accumulate_fn=np.add)
idf = expr.log(data.shape[0] * 1.0 / (df + 1)) + 1
# Normalized Frequency for a feature in a document is calculated by dividing the feature frequency
# by the root mean square of features frequencies in that document
square_sum = expr.reduce(data,
axis=1,
dtype_fn=lambda input: input.dtype,
local_reduce_fn=lambda ex, data, axis: np.square(data).sum(axis),
accumulate_fn=np.add)
rms = expr.sqrt(square_sum * 1.0 / data.shape[1])
# calculate weight normalized Tf-Idf
data = data / rms.reshape((data.shape[0], 1)) * idf.reshape((1, data.shape[1]))
# add up all the feature vectors with the same labels
#weights_per_label_and_feature = expr.ndarray((label_size, data.shape[1]), dtype=np.float64)
#for i in range(label_size):
# i_mask = (labels == i)
# weights_per_label_and_feature = expr.assign(weights_per_label_and_feature, np.s_[i, :], expr.sum(data[i_mask, :], axis=0))
weights_per_label_and_feature = expr.shuffle(expr.retile(data, tile_hint=util.calc_tile_hint(data, axis=0)),
_sum_instance_by_label_mapper,
target=expr.ndarray((label_size, data.shape[1]), dtype=np.float64, reduce_fn=np.add),
kw={'labels': labels, 'label_size': label_size},
cost_hint={hash(labels):{'00':0, '01':np.prod(labels.shape)}})
# sum up all the weights for each label from the previous step
weights_per_label = expr.sum(weights_per_label_and_feature, axis=1)
# generate naive bayes per_label_and_feature weights
weights_per_label_and_feature = expr.log((weights_per_label_and_feature + alpha) /
(weights_per_label.reshape((weights_per_label.shape[0], 1)) +
alpha * weights_per_label_and_feature.shape[1]))
return {'scores_per_label_and_feature': weights_per_label_and_feature.optimized().force(),
'scores_per_label': weights_per_label.optimized().force(),
}
def fit(self, X, y):
""" Transform to distarray if it's numpy array"""
if isinstance(X, np.ndarray):
X = expr.make_from_numpy(X)
if isinstance(y, np.ndarray):
y = expr.make_from_numpy(y)
X, y, X_mean, y_mean, X_std = self._center_data(
X, y, self.fit_intercept, self.normalize)
N_DIM = X.shape[1]
self._coef = np.random.randn(N_DIM, 1)
for i in range(self.iterations):
yp = expr.dot(X, self._coef)
print expr.sum((yp - y) ** 2).glom()
diff = X * (yp - y)
grad = expr.sum(diff, axis=0).glom().reshape((N_DIM, 1))
self._coef = self._coef - grad * 1e-6
return self
def _get_norm_of_each_item(self, rating_table):
"""Get norm of each item vector.
For each Item, caculate the norm the item vector.
Parameters
----------
rating_table : Spartan matrix of shape(M, N).
Each column represents the rating of the item.
Returns
---------
item_norm: Spartan matrix of shape(N,).
item_norm[i] equals || rating_table[:,i] ||
"""
return expr.sqrt(expr.sum(expr.multiply(rating_table, rating_table), axis=0))
def _get_norm_of_each_item(self, rating_table):
"""Get norm of each item vector.
For each Item, caculate the norm the item vector.
Parameters
----------
rating_table : Spartan matrix of shape(M, N).
Each column represents the rating of the item.
Returns
---------
item_norm: Spartan matrix of shape(N,).
item_norm[i] equals || rating_table[:,i] ||
"""
ctx = blob_ctx.get()
if isinstance(rating_table, array.distarray.DistArray):
rating_table = expr.lazify(rating_table)
res = expr.sqrt(expr.sum(expr.multiply(rating_table, rating_table), axis=0,
tile_hint=(rating_table.shape[1] / ctx.num_workers, )))
return res.force()
def fit(data, labels, T=50, la=1.0):
'''
Train an SVM model using the disdca (2013) algorithm.
Args:
data(Expr): points to be trained.
labels(Expr): the correct labels of the training data.
T(int): max training iterations.
la(float): lambda parameter of this SVM model.
'''
w = expr.zeros((data.shape[1], 1), dtype=np.float64)
alpha = expr.zeros((data.shape[0], 1), dtype=np.float64)
for i in range(T):
alpha = expr.shuffle(expr.retile(data, tile_hint=util.calc_tile_hint(data, axis=0)),
_svm_mapper,
kw={'labels': labels, 'alpha': alpha, 'w': w, 'lambda_n': la * data.shape[0]},
shape_hint=alpha.shape,
cost_hint={ hash(labels) : {'00': 0, '01': np.prod(labels.shape)}, hash(alpha) : {'00': 0, '01': np.prod(alpha.shape)} })
w = expr.sum(data * alpha * 1.0 / la / data.shape[0], axis=0).reshape((data.shape[1], 1))
w = w.optimized()
return w
def als(A, la=0.065, alpha=40, implicit_feedback=False, num_features=20, num_iter=10, M=None):
'''
compute the factorization A = U M' using the alternating least-squares (ALS) method.
where `A` is the "ratings" matrix which maps from a user and item to a rating score,
`U` and `M` are the factor matrices, which represent user and item preferences.
Args:
A(Expr or DistArray): the rating matrix which maps from a user and item to a rating score.
la(float): the parameter of the als.
alpha(int): confidence parameter used on implicit feedback.
implicit_feedback(bool): whether using implicit_feedback method for als.
num_features(int): dimension of the feature space.
num_iter(int): max iteration to run.
'''
num_users = A.shape[0]
num_items = A.shape[1]
AT = expr.transpose(A)
avg_rating = expr.sum(A, axis=0) * 1.0 / expr.count_nonzero(A, axis=0)
M = expr.rand(num_items, num_features)
M = expr.assign(M, np.s_[:, 0], avg_rating.reshape((avg_rating.shape[0], 1)))
#A = expr.retile(A, tile_hint=util.calc_tile_hint(A, axis=0))
#AT = expr.retile(AT, tile_hint=util.calc_tile_hint(AT, axis=0))
for i in range(num_iter):
# Recomputing U
shape = (num_users, num_features)
U = expr.outer((A, M), (0, None), fn=_solve_U_or_M_mapper,
fn_kw={'la': la, 'alpha': alpha,
'implicit_feedback': implicit_feedback, 'shape': shape},
shape=shape, dtype=np.float)
# Recomputing M
shape = (num_items, num_features)
M = expr.outer((AT, U), (0, None), fn=_solve_U_or_M_mapper,
fn_kw={'la': la, 'alpha': alpha,
'implicit_feedback': implicit_feedback, 'shape': shape},
shape=shape, dtype=np.float)
return U, M
def test_optimization_reduced(self):
na = np.random.rand(1000, 1000)
nb = np.random.rand(1000, 1000)
a = expr.from_numpy(na)
b = expr.from_numpy(nb)
c = a - b
d = a + c
f = c[200:900, 200:900]
g = d[200:900, 200:900]
h = f - g
i = f + h
j = h[100:500, 100:500]
k = i[100:500, 100:500]
l = expr.dot(j, k)
m = j + k
n = k - l
o = n - m
q = n + o
r = q - m
s = expr.sum(r)
nc = na - nb
nd = na + nc
nf = nc[200:900, 200:900]
ng = nd[200:900, 200:900]
nh = nf - ng
ni = nf + nh
nj = nh[100:500, 100:500]
nk = ni[100:500, 100:500]
nl = np.dot(nj, nk)
nm = nj + nk
nn = nk - nl
no = nn - nm
nq = nn + no
nr = nq - nm
ns = np.sum(nr)
# Our sum seems to reduce precision
Assert.all_eq(ns, s.optimized().glom(), tolerance = 1e-6)
def als(A, la=0.065, alpha=40, implicit_feedback=False, num_features=20, num_iter=10):
'''
compute the factorization A = U M' using the alternating least-squares (ALS) method.
where `A` is the "ratings" matrix which maps from a user and item to a rating score,
`U` and `M` are the factor matrices, which represent user and item preferences.
Args:
A(Expr or DistArray): the rating matrix which maps from a user and item to a rating score.
la(float): the parameter of the als.
alpha(int): confidence parameter used on implicit feedback.
implicit_feedback(bool): whether using implicit_feedback method for als.
num_features(int): dimension of the feature space.
num_iter(int): max iteration to run.
'''
A = expr.force(A)
AT = expr.shuffle(expr.ndarray((A.shape[1], A.shape[0]), dtype=A.dtype,
tile_hint=(A.shape[1] / len(A.tiles), A.shape[0])),
_transpose_mapper, kw={'orig_array': A})
num_items = A.shape[1]
avg_rating = expr.sum(A, axis=0, tile_hint=(num_items / len(A.tiles),)) * 1.0 / \
expr.count_nonzero(A, axis=0, tile_hint=(num_items / len(A.tiles),))
M = expr.shuffle(expr.ndarray((num_items, num_features),
tile_hint=(num_items / len(A.tiles), num_features)),
_init_M_mapper, kw={'avg_rating': avg_rating})
#util.log_warn('avg_rating:%s M:%s', avg_rating.glom(), M.glom())
for i in range(num_iter):
# Recomputing U
U = expr.shuffle(A, _solve_U_or_M_mapper, kw={'U_or_M': M, 'la': la, 'alpha': alpha, 'implicit_feedback': implicit_feedback})
# Recomputing M
M = expr.shuffle(AT, _solve_U_or_M_mapper, kw={'U_or_M': U, 'la': la, 'alpha': alpha, 'implicit_feedback': implicit_feedback})
return U, M
def test_linear_reg(self):
_skip_if_travis()
N_EXAMPLES = 10 * 1000 * 1000 * self.ctx.num_workers
N_DIM = 10
x = expr.rand(N_EXAMPLES, N_DIM,
tile_hint=(N_EXAMPLES / self.ctx.num_workers, N_DIM)).astype(np.float32)
y = expr.rand(N_EXAMPLES, 1,
tile_hint=(N_EXAMPLES / self.ctx.num_workers, 1)).astype(np.float32)
w = np.random.rand(N_DIM, 1).astype(np.float32)
x = expr.eager(x)
y = expr.eager(y)
start = time.time()
for i in range(5):
yp = expr.dot(x, w)
diff = x * (yp - y)
grad = expr.sum(diff, axis=0, tile_hint=[N_DIM]).glom().reshape((N_DIM, 1))
w = w - grad * 1e-6
cost = time.time() - start
self._verify_cost("linear_reg", cost)
def spectral_cluster(points, k=10, num_iter=10, similarity_measurement='rbf'):
'''
clustering data points using kmeans spectral clustering method.
Args:
points(Expr or DistArray): the data points to be clustered.
k(int): the number of clusters we need to generate.
num_iter(int): the max number of iterations that kmeans clustering method runs.
similarity_measurement(str): distance method used to measure similarity between two points.
'''
# calculate similarity for each pair of points to generate the adjacency matrix A
A = expr.shuffle(points, _row_similarity_mapper, kw={'similarity_measurement': similarity_measurement})
num_dims = A.shape[1]
# Construct the diagonal matrix D
D = expr.sum(A, axis=1, tile_hint=(A.shape[0],))
# Calculate the normalized Laplacian of the form: L = D^(-0.5)AD^(-0.5)
L = expr.shuffle(A, _laplacian_mapper, kw={'D': D})
# Perform eigen-decomposition using Lanczos solver
overshoot = min(k * 2, num_dims)
d, U = lanczos.solve(L, L, overshoot, True)
U = U[:, 0:k]
# Generate initial clusters which picks rows as centers if that row contains max eigen
# value in that column
init_clusters = U[np.argmax(U, axis=0)]
# Run kmeans clustering with init_clusters
kmeans = KMeans(k, num_iter)
U = expr.from_numpy(U)
centers, labels = kmeans.fit(U, init_clusters)
return labels
def center_data(X, y, fit_intercept, normalize=False):
"""
Centers data to have mean zero along axis 0. This is here because
nearly all linear models will want their data to be centered.
"""
if fit_intercept:
X_mean = X.mean(axis = 0)
X_mean = expr.reshape(X_mean, (1, X_mean.shape[0]))
X -= X_mean
if normalize:
X_std = expr.sqrt(expr.sum(X ** 2, axis=0)).force()
X_std[X_std == 0] = 1
X /= X_std
else:
X_std = expr.ones(X.shape[1])
y_mean = y.mean(axis=0)
y -= y_mean
else:
X_mean = expr.zeros(X.shape[1])
X_std = expr.ones(X.shape[1])
y_mean = 0. if y.ndim == 1 else expr.zeros(y.shape[1], dtype=X.dtype)
return X, y, X_mean, y_mean, X_std
def kneighbors(self, X, n_neighbors=None):
"""Finds the K-neighbors of a point.
Returns distance
Parameters
----------
X : array-like, last dimension same as that of fit data
The new point.
n_neighbors : int
Number of neighbors to get (default is the value
passed to the constructor).
Returns
-------
dist : array
Array representing the lengths to point, only present if
return_distance=True
ind : array
Indices of the nearest points in the population matrix.
"""
if n_neighbors is not None:
self.n_neighbors = n_neighbors
if isinstance(X, np.ndarray):
X = expr.from_numpy(X)
if self.algorithm in ('auto', 'brute'):
X_broadcast = expr.reshape(X, (X.shape[0], 1, X.shape[1]))
fit_X_broadcast = expr.reshape(
self.X, (1, self.X.shape[0], self.X.shape[1]))
distances = expr.sum((X_broadcast - fit_X_broadcast)**2, axis=2)
neigh_ind = expr.argsort(distances, axis=1)
neigh_ind = neigh_ind[:, :n_neighbors].optimized().glom()
neigh_dist = expr.sort(distances, axis=1)
neigh_dist = expr.sqrt(
neigh_dist[:, :n_neighbors]).optimized().glom()
return neigh_dist, neigh_ind
else:
results = self.X.foreach_tile(mapper_fn=_knn_mapper,
kw={
'X': self.X,
'Q': X,
'n_neighbors': self.n_neighbors,
'algorithm': self.algorithm
})
dist = None
ind = None
""" Get the KNN candidates for each tile of X, then find out the real KNN """
for k, v in results.iteritems():
if dist is None:
dist = v[0]
ind = v[1]
else:
dist = np.concatenate((dist, v[0]), axis=1)
ind = np.concatenate((ind, v[1]), axis=1)
mask = np.argsort(dist, axis=1)[:, :self.n_neighbors]
new_dist = np.array([dist[i][mask[i]] for i, r in enumerate(dist)])
new_ind = np.array([ind[i][mask[i]] for i, r in enumerate(ind)])
return new_dist, new_ind
def simulate(ts_all, te_all, lamb_all, num_paths):
"""Range over a number of independent products.
:param ts_all: DistArray
Start dates for a series of swaptions.
:param te_all: DistArray
End dates for a series of swaptions.
:param lamb_all: DistArray
Parameter values for a series of swaptions.
:param num_paths: Int
Number of paths used in random walk.
:rtype: DistArray
"""
swaptions = []
i = 0
for ts_a, te, lamb in zip(ts_all, te_all, lamb_all):
for ts in ts_a:
# start = time()
print i
time_structure = arange(None, 0, ts + DELTA, DELTA)
maturity_structure = arange(None, 0, te, DELTA)
############# MODEL ###############
# Variance reduction technique - Antithetic Variates.
eps_tmp = randn(time_structure.shape[0] - 1, num_paths)
eps = concatenate(eps_tmp, -eps_tmp, 1)
# Forward LIBOR rates for the construction of the spot measure.
f_kk = zeros((time_structure.shape[0], 2 * num_paths))
f_kk = assign(f_kk, np.s_[0, :], F_0)
# Plane kxN of simulated LIBOR rates.
f_kn = ones((maturity_structure.shape[0], 2 * num_paths)) * F_0
# Simulations of the plane f_kn for each time step.
for t in xrange(1, time_structure.shape[0]):
f_kn_new = f_kn[1:, :] * exp(
lamb * mu(f_kn, lamb) * DELTA - 0.5 * lamb * lamb * DELTA + lamb * eps[t - 1, :] * sqrt(DELTA)
)
f_kk = assign(f_kk, np.s_[t, :], f_kn_new[0])
f_kn = f_kn_new
############## PRODUCT ###############
# Value of zero coupon bonds.
zcb = ones((int((te - ts) / DELTA) + 1, 2 * num_paths))
f_kn_modified = 1 + DELTA * f_kn
for j in xrange(zcb.shape[0] - 1):
zcb = assign(zcb, np.s_[j + 1], zcb[j] / f_kn_modified[j])
# Swaption price at maturity.
last_row = zcb[zcb.shape[0] - 1, :].reshape((20,))
swap_ts = maximum(1 - last_row - THETA * DELTA * expr.sum(zcb[1:], 0), 0)
# Spot measure used for discounting.
b_ts = ones((2 * num_paths,))
tmp = 1 + DELTA * f_kk
for j in xrange(int(ts / DELTA)):
b_ts *= tmp[j].reshape((20,))
# Swaption price at time 0.
swaption = swap_ts / b_ts
# Save expected value in bps and std.
me = mean((swaption[0:num_paths] + swaption[num_paths:]) / 2) * 10000
st = std((swaption[0:num_paths] + swaption[num_paths:]) / 2) / sqrt(num_paths) * 10000
swaptions.append([me.optimized().force(), st.optimized().force()])
# print time() - start
i += 1
return swaptions
def fn3():
a = expr.ones((N, N))
b = expr.ones((N, N / 2))
g = expr.dot(a, b) + expr.dot(expr.sum(a, axis=1).reshape((1, N)), b)
return g
def gen_reduce(a):
if hasattr(a, 'shape') and len(a.shape) > 0:
return [expr.sum(a, axis=random.randrange(len(a.shape)))]
return [a]
def als(A,
la=0.065,
alpha=40,
implicit_feedback=False,
num_features=20,
num_iter=10,
M=None):
'''
compute the factorization A = U M' using the alternating least-squares (ALS) method.
where `A` is the "ratings" matrix which maps from a user and item to a rating score,
`U` and `M` are the factor matrices, which represent user and item preferences.
Args:
A(Expr or DistArray): the rating matrix which maps from a user and item to a rating score.
la(float): the parameter of the als.
alpha(int): confidence parameter used on implicit feedback.
implicit_feedback(bool): whether using implicit_feedback method for als.
num_features(int): dimension of the feature space.
num_iter(int): max iteration to run.
'''
num_users = A.shape[0]
num_items = A.shape[1]
AT = expr.transpose(A)
avg_rating = expr.sum(A, axis=0) * 1.0 / expr.count_nonzero(A, axis=0)
M = expr.rand(num_items, num_features)
M = expr.assign(M, np.s_[:, 0], avg_rating.reshape(
(avg_rating.shape[0], 1)))
#A = expr.retile(A, tile_hint=util.calc_tile_hint(A, axis=0))
#AT = expr.retile(AT, tile_hint=util.calc_tile_hint(AT, axis=0))
for i in range(num_iter):
# Recomputing U
shape = (num_users, num_features)
U = expr.outer(
(A, M), (0, None),
fn=_solve_U_or_M_mapper,
fn_kw={
'la': la,
'alpha': alpha,
'implicit_feedback': implicit_feedback,
'shape': shape
},
shape=shape,
dtype=np.float)
# Recomputing M
shape = (num_items, num_features)
M = expr.outer(
(AT, U), (0, None),
fn=_solve_U_or_M_mapper,
fn_kw={
'la': la,
'alpha': alpha,
'implicit_feedback': implicit_feedback,
'shape': shape
},
shape=shape,
dtype=np.float)
return U, M
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 = expr.force(points)
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 = expr.as_array(centers)
points_broadcast = expr.reshape(points,
(points.shape[0], 1, points.shape[1]))
centers_broadcast = expr.reshape(
centers, (1, centers.shape[0], centers.shape[1]))
distances = expr.sum(expr.square(points_broadcast - centers_broadcast),
axis=2)
# This is used to avoid dividing zero
distances = distances + 0.00000000001
util.log_info('distances shape %s' % str(distances.shape))
distances_broadcast = expr.reshape(
distances, (distances.shape[0], 1, distances.shape[1]))
distances_broadcast2 = expr.reshape(
distances, (distances.shape[0], distances.shape[1], 1))
prob = 1.0 / expr.sum(expr.power(
distances_broadcast / distances_broadcast2, 2.0 / (m - 1)),
axis=2)
prob.force()
counts = expr.sum(prob, axis=0)
counts = expr.reshape(counts, (counts.shape[0], 1))
labels = expr.argmax(prob, axis=1)
centers = expr.sum(
expr.reshape(points, (points.shape[0], 1, points.shape[1])) *
expr.reshape(prob, (prob.shape[0], prob.shape[1], 1)),
axis=0)
# We assume that the size of centers are relative small that can be handled
# on the master.
counts = counts.glom()
centers = centers.glom()
# If any centroids don't have any points assigned to them.
zcount_indices = (counts == 0).reshape(k)
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
# and set their counts to 1 in order to get rid of dividing by zero.
counts[zcount_indices, :] = 1
centers[zcount_indices, :] = np.random.rand(
np.count_nonzero(zcount_indices), num_dim)
centers = centers / counts
return labels
def train(self):
for i in range(self.iterations):
diff = self.update()
grad = expr.sum(diff, axis=0).optimized().glom().reshape((self.N_DIM, 1))
self.w = self.w - grad * self.alpha
return self.w
def simulate(ts_all, te_all, lamb_all, num_paths):
'''Range over a number of independent products.
:param ts_all: DistArray
Start dates for a series of swaptions.
:param te_all: DistArray
End dates for a series of swaptions.
:param lamb_all: DistArray
Parameter values for a series of swaptions.
:param num_paths: Int
Number of paths used in random walk.
:rtype: DistArray
'''
swaptions = []
i = 0
for ts_a, te, lamb in zip(ts_all, te_all, lamb_all):
for ts in ts_a:
#start = time()
print i
time_structure = arange(None, 0, ts + DELTA, DELTA)
maturity_structure = arange(None, 0, te, DELTA)
############# MODEL ###############
# Variance reduction technique - Antithetic Variates.
eps_tmp = randn(time_structure.shape[0] - 1, num_paths)
eps = concatenate(eps_tmp, -eps_tmp, 1)
# Forward LIBOR rates for the construction of the spot measure.
f_kk = zeros((time_structure.shape[0], 2*num_paths))
f_kk = assign(f_kk, np.s_[0, :], F_0)
# Plane kxN of simulated LIBOR rates.
f_kn = ones((maturity_structure.shape[0], 2*num_paths))*F_0
# Simulations of the plane f_kn for each time step.
for t in xrange(1, time_structure.shape[0]):
f_kn_new = f_kn[1:, :]*exp(lamb*mu(f_kn, lamb)*DELTA-0.5*lamb*lamb *
DELTA + lamb*eps[t - 1, :]*sqrt(DELTA))
f_kk = assign(f_kk, np.s_[t, :], f_kn_new[0])
f_kn = f_kn_new
############## PRODUCT ###############
# Value of zero coupon bonds.
zcb = ones((int((te-ts)/DELTA)+1, 2*num_paths))
f_kn_modified = 1 + DELTA*f_kn
for j in xrange(zcb.shape[0] - 1):
zcb = assign(zcb, np.s_[j + 1], zcb[j] / f_kn_modified[j])
# Swaption price at maturity.
last_row = zcb[zcb.shape[0] - 1, :].reshape((20, ))
swap_ts = maximum(1 - last_row - THETA*DELTA*expr.sum(zcb[1:], 0), 0)
# Spot measure used for discounting.
b_ts = ones((2*num_paths, ))
tmp = 1 + DELTA * f_kk
for j in xrange(int(ts/DELTA)):
b_ts *= tmp[j].reshape((20, ))
# Swaption price at time 0.
swaption = swap_ts/b_ts
# Save expected value in bps and std.
me = mean((swaption[0:num_paths] + swaption[num_paths:])/2) * 10000
st = std((swaption[0:num_paths] + swaption[num_paths:])/2)/sqrt(num_paths)*10000
swaptions.append([me.optimized().force(), st.optimized().force()])
#print time() - start
i += 1
return swaptions
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
def gen_reduce(a):
if hasattr(a, 'shape') and len(a.shape) > 0:
return [expr.sum(a, axis=random.randrange(len(a.shape)))]
return [a]
def fit(data, labels, label_size, alpha=1.0):
'''
Train standard naive bayes model.
Args:
data(Expr): documents to be trained.
labels(Expr): the correct labels of the training data.
label_size(int): the number of different labels.
alpha(float): alpha parameter of naive bayes model.
'''
# calc document freq
df = expr.reduce(data,
axis=0,
dtype_fn=lambda input: input.dtype,
local_reduce_fn=lambda ex, data, axis:
(data > 0).sum(axis),
accumulate_fn=np.add)
idf = expr.log(data.shape[0] * 1.0 / (df + 1)) + 1
# Normalized Frequency for a feature in a document is calculated by dividing the feature frequency
# by the root mean square of features frequencies in that document
square_sum = expr.reduce(
data,
axis=1,
dtype_fn=lambda input: input.dtype,
local_reduce_fn=lambda ex, data, axis: np.square(data).sum(axis),
accumulate_fn=np.add)
rms = expr.sqrt(square_sum * 1.0 / data.shape[1])
# calculate weight normalized Tf-Idf
data = data / rms.reshape((data.shape[0], 1)) * idf.reshape(
(1, data.shape[1]))
# add up all the feature vectors with the same labels
#weights_per_label_and_feature = expr.ndarray((label_size, data.shape[1]), dtype=np.float64)
#for i in range(label_size):
# i_mask = (labels == i)
# weights_per_label_and_feature = expr.assign(weights_per_label_and_feature, np.s_[i, :], expr.sum(data[i_mask, :], axis=0))
weights_per_label_and_feature = expr.shuffle(
expr.retile(data, tile_hint=util.calc_tile_hint(data, axis=0)),
_sum_instance_by_label_mapper,
target=expr.ndarray((label_size, data.shape[1]),
dtype=np.float64,
reduce_fn=np.add),
kw={
'labels': labels,
'label_size': label_size
},
cost_hint={hash(labels): {
'00': 0,
'01': np.prod(labels.shape)
}})
# sum up all the weights for each label from the previous step
weights_per_label = expr.sum(weights_per_label_and_feature, axis=1)
# generate naive bayes per_label_and_feature weights
weights_per_label_and_feature = expr.log(
(weights_per_label_and_feature + alpha) /
(weights_per_label.reshape((weights_per_label.shape[0], 1)) +
alpha * weights_per_label_and_feature.shape[1]))
return {
'scores_per_label_and_feature':
weights_per_label_and_feature.optimized().force(),
'scores_per_label':
weights_per_label.optimized().force(),
}
def fn3(): a = expr.ones((N, N)) b = expr.ones((N, N/2)) g = expr.dot(a, b) + expr.dot(expr.sum(a, axis=1).reshape((1, N)), b) return g
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
