def train_smo_1998(self, data, labels):
'''
Train an SVM model using the SMO (1998) algorithm.
Args:
data(Expr): points to be trained
labels(Expr): the correct labels of the training data
'''
N = data.shape[0] # Number of instances
D = data.shape[1] # Number of features
self.b = 0.0
self.alpha = expr.zeros((N,1), dtype=np.float64, tile_hint=[N/self.ctx.num_workers, 1]).force()
# linear kernel
kernel_results = expr.dot(data, expr.transpose(data), tile_hint=[N/self.ctx.num_workers, N])
labels = expr.force(labels)
self.E = expr.zeros((N,1), dtype=np.float64, tile_hint=[N/self.ctx.num_workers, 1]).force()
for i in xrange(N):
self.E[i, 0] = self.b + expr.reduce(self.alpha, axis=None, dtype_fn=lambda input: input.dtype,
local_reduce_fn=margin_mapper,
accumulate_fn=np.add,
fn_kw=dict(label=labels, data=kernel_results[:,i].force())).glom() - labels[i, 0]
util.log_info("Starting SMO")
it = 0
num_changed = 0
examine_all = True
while (num_changed > 0 or examine_all) and (it < self.maxiter):
util.log_info("Iteration:%d", it)
num_changed = 0
if examine_all:
for i in xrange(N):
num_changed += self.examine_example(i, N, labels, kernel_results)
else:
for i in xrange(N):
if self.alpha[i, 0] > 0 and self.alpha[i, 0] < self.C:
num_changed += self.examine_example(i, N, labels, kernel_results)
it += 1
if examine_all: examine_all = False
elif num_changed == 0: examine_all = True
self.w = expr.zeros((D, 1), dtype=np.float64).force()
for i in xrange(D):
self.w[i,0] = expr.reduce(self.alpha, axis=None, dtype_fn=lambda input: input.dtype,
local_reduce_fn=margin_mapper,
accumulate_fn=np.add,
fn_kw=dict(label=labels, data=expr.force(data[:,i]))).glom()
self.usew_ = True
print 'iteration finish:', it
print 'b:', self.b
print 'w:', self.w.glom()
def random_galaxy(n):
'''Generate a galaxy of random bodies.'''
dtype = np.float # consistent with sp.rand, same as np.float64
galaxy = { # All bodies stand still initially.
'm': (rand(n) + dtype(10)) * dtype(m_sol / 10),
'x': (rand(n) - dtype(0.5)) * dtype(r_ly / 100),
'y': (rand(n) - dtype(0.5)) * dtype(r_ly / 100),
'z': (rand(n) - dtype(0.5)) * dtype(r_ly / 100),
'vx': zeros((n, )),
'vy': zeros((n, )),
'vz': zeros((n, ))
}
return galaxy
def random_galaxy(n):
'''Generate a galaxy of random bodies.'''
dtype = np.float # consistent with sp.rand, same as np.float64
galaxy = { # All bodies stand still initially.
'm': (rand(n) + dtype(10)) * dtype(m_sol/10),
'x': (rand(n) - dtype(0.5)) * dtype(r_ly/100),
'y': (rand(n) - dtype(0.5)) * dtype(r_ly/100),
'z': (rand(n) - dtype(0.5)) * dtype(r_ly/100),
'vx': zeros((n, )),
'vy': zeros((n, )),
'vz': zeros((n, ))
}
return galaxy
def precompute(self):
'''Precompute the most k similar items for each item.
After this funcion returns. 2 attributes will be created.
Attributes
------
top_k_similar_table : Numpy array of shape (N, k).
Records the most k similar scores between each items.
top_k_similar_indices : Numpy array of shape (N, k).
Records the indices of most k similar items for each item.
'''
M = self.rating_table.shape[0]
N = self.rating_table.shape[1]
self.rating_table = expr.force(self.rating_table)
assert self.rating_table.tile_shape()[0] == M, \
"rating table is only allowed to tile by columns!"
self.similarity_table = expr.zeros(shape=(N, N),
tile_hint=(self.rating_table.tile_shape()[1], N)).force()
self.item_norm = self._get_norm_of_each_item(self.rating_table)
self.rating_table.foreach_tile(mapper_fn=_similarity_mapper,
kw={'rating_table' : self.rating_table,
'similarity_table' : self.similarity_table,
'item_norm' : self.item_norm,
'step' : self.rating_table.tile_shape()[1]})
# Release the memory for item_norm
self.item_norm = None
k = self.k
top_k_similar_table = expr.zeros((N, k),
tile_hint=(self.rating_table.tile_shape()[1], k)).force()
top_k_similar_indices = expr.zeros((N, k),
tile_hint=(self.rating_table.tile_shape()[1], k),
dtype=np.int).force()
# Find top-k similar items for each item.
# Store the similarity scores into table top_k_similar table.
# Store the indices of top k items into table top_k_similar_indices.
self.similarity_table.foreach_tile(mapper_fn=_select_most_k_similar_mapper,
kw={'similarity_table' : self.similarity_table,
'top_k_similar_table' : top_k_similar_table,
'top_k_similar_indices' : top_k_similar_indices,
'k' : k})
self.top_k_similar_table = top_k_similar_table.glom()
self.top_k_similar_indices = top_k_similar_indices.glom()
def benchmark_optimization(ctx, timer):
FLAGS.optimization = 0
DATA_SIZE = 5 * 1000 * 1000
current = eager(zeros((DATA_SIZE * ctx.num_workers,),
dtype=np.float32, tile_hint = (DATA_SIZE,)))
strike = eager(ones((DATA_SIZE * ctx.num_workers,),
dtype=np.float32, tile_hint=(DATA_SIZE,)))
maturity = eager(strike * 12)
rate = eager(strike * 0.05)
volatility = eager(strike * 0.01)
timer.time_op('opt-none', lambda: bs_step(current, strike, maturity, rate, volatility))
timer.time_op('opt-none', lambda: bs_step(current, strike, maturity, rate, volatility))
timer.time_op('opt-none', lambda: bs_step(current, strike, maturity, rate, volatility))
FLAGS.optimization = 1
FLAGS.opt_parakeet_gen = 0
FLAGS.opt_map_fusion = 1
timer.time_op('opt-fusion', lambda: bs_step(current, strike, maturity, rate, volatility))
timer.time_op('opt-fusion', lambda: bs_step(current, strike, maturity, rate, volatility))
timer.time_op('opt-fusion', lambda: bs_step(current, strike, maturity, rate, volatility))
FLAGS.opt_parakeet_gen = 1
timer.time_op('opt-parakeet', lambda: bs_step(current, strike, maturity, rate, volatility))
timer.time_op('opt-parakeet', lambda: bs_step(current, strike, maturity, rate, volatility))
timer.time_op('opt-parakeet', lambda: bs_step(current, strike, maturity, rate, volatility))
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 jacobi_method(A, b, _iter=100):
"""
Iterative algorithm for approximating the solutions of a diagonally dominant system of linear equations.
Parameters
----------
A : ndarray or Expr - 2d
Input matrix
b : ndarray or Expr - vector
RHS vector
_iter : int
Times of iteration needed, default to be 100
Returns
-------
result : Expr - vector
Approximated solution.
"""
util.Assert.eq(A.shape[0], b.shape[0])
x = expr.zeros((A.shape[0],))
D = expr.diag(A)
R = A - expr.diagflat(D)
for i in xrange(_iter):
x = (b - expr.dot(R, x)) / D
return x
def jacobi_method(A, b, _iter=100):
"""
Iterative algorithm for approximating the solutions of a diagonally dominant system of linear equations.
Parameters
----------
A : ndarray or Expr - 2d
Input matrix
b : ndarray or Expr - vector
RHS vector
_iter : int
Times of iteration needed, default to be 100
Returns
-------
result : Expr - vector
Approximated solution.
"""
util.Assert.eq(A.shape[0], b.shape[0])
x = expr.zeros((A.shape[0], ))
D = expr.diag(A)
R = A - expr.diagflat(D)
for i in xrange(_iter):
x = (b - expr.dot(R, x)) / D
return x
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)
r = x
rho = expr.sum(r * r).optimized().glom()
#util.log_warn('rho:%s', rho)
p = r
for i in xrange(15):
q = expr.dot(A, p)
alpha = rho / expr.sum(p * q).optimized().glom()
#util.log_warn('alpha:%s', alpha)
z = z + p * alpha
rho0 = rho
r = r - q * alpha
rho = expr.sum(r * r).optimized().glom()
beta = rho / rho0
#util.log_warn('beta:%s', beta)
p = r + p * beta
return z
def precompute(self):
'''Precompute the most k similar items for each item.
After this funcion returns. 2 attributes will be created.
Attributes
------
top_k_similar_table : Numpy array of shape (N, k).
Records the most k similar scores between each items.
top_k_similar_indices : Numpy array of shape (N, k).
Records the indices of most k similar items for each item.
'''
M = self.rating_table.shape[0]
N = self.rating_table.shape[1]
self.similarity_table = expr.shuffle(self.rating_table, _similarity_mapper,
kw={'item_norm': self._get_norm_of_each_item(self.rating_table),
'step': util.divup(self.rating_table.shape[1], blob_ctx.get().num_workers)},
shape_hint=(N, N))
# Release the memory for item_norm
top_k_similar_indices = expr.zeros((N, self.k), dtype=np.int)
# Find top-k similar items for each item.
# Store the similarity scores into table top_k_similar table.
# Store the indices of top k items into table top_k_similar_indices.
cost = np.prod(top_k_similar_indices.shape)
top_k_similar_table = expr.shuffle(self.similarity_table, _select_most_k_similar_mapper,
kw = {'top_k_similar_indices': top_k_similar_indices, 'k': self.k},
shape_hint=(N, self.k),
cost_hint={hash(top_k_similar_indices):{'00': 0, '01': cost, '10': cost, '11': cost}})
self.top_k_similar_table = top_k_similar_table.optimized().glom()
self.top_k_similar_indices = top_k_similar_indices.optimized().glom()
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 benchmark_optimization(ctx, timer):
FLAGS.optimization = 0
DATA_SIZE = 5 * 1000 * 1000
current = eager(
zeros((DATA_SIZE * ctx.num_workers, ),
dtype=np.float32,
tile_hint=(DATA_SIZE, )))
strike = eager(
ones((DATA_SIZE * ctx.num_workers, ),
dtype=np.float32,
tile_hint=(DATA_SIZE, )))
maturity = eager(strike * 12)
rate = eager(strike * 0.05)
volatility = eager(strike * 0.01)
timer.time_op('opt-none',
lambda: bs_step(current, strike, maturity, rate, volatility))
timer.time_op('opt-none',
lambda: bs_step(current, strike, maturity, rate, volatility))
timer.time_op('opt-none',
lambda: bs_step(current, strike, maturity, rate, volatility))
FLAGS.optimization = 1
FLAGS.opt_parakeet_gen = 0
FLAGS.opt_map_fusion = 1
timer.time_op('opt-fusion',
lambda: bs_step(current, strike, maturity, rate, volatility))
timer.time_op('opt-fusion',
lambda: bs_step(current, strike, maturity, rate, volatility))
timer.time_op('opt-fusion',
lambda: bs_step(current, strike, maturity, rate, volatility))
FLAGS.opt_parakeet_gen = 1
timer.time_op('opt-parakeet',
lambda: bs_step(current, strike, maturity, rate, volatility))
timer.time_op('opt-parakeet',
lambda: bs_step(current, strike, maturity, rate, volatility))
timer.time_op('opt-parakeet',
lambda: bs_step(current, strike, maturity, rate, volatility))
FLAGS.opt_parakeet_gen = 0
FLAGS.opt_auto_tiling = 0
timer.time_op('opt-tiling = 0',
lambda: bs_step(current, strike, maturity, rate, volatility))
timer.time_op('opt-tiling = 0',
lambda: bs_step(current, strike, maturity, rate, volatility))
timer.time_op('opt-tiling = 0',
lambda: bs_step(current, strike, maturity, rate, volatility))
FLAGS.opt_auto_tiling = 1
timer.time_op('opt-tiling',
lambda: bs_step(current, strike, maturity, rate, volatility))
timer.time_op('opt-tiling',
lambda: bs_step(current, strike, maturity, rate, volatility))
timer.time_op('opt-tiling',
lambda: bs_step(current, strike, maturity, rate, volatility))
def benchmark_slice(ctx, timer):
TEST_SIZE = 1000 * ctx.num_workers
# force arange to evaluate first.
x = expr.eager(expr.zeros((TEST_SIZE,10000)))
for i in range(5):
timer.time_op('slice-rows', lambda: expr.evaluate(x[200:300, :].sum()))
timer.time_op('slice-cols', lambda: expr.evaluate(x[:, 200:300].sum()))
timer.time_op('slice-box', lambda: expr.evaluate(x[200:300, 200:300].sum()))
def benchmark_slice(ctx, timer):
TEST_SIZE = 1000 * ctx.num_workers
# force arange to evaluate first.
x = expr.eager(expr.zeros((TEST_SIZE, 10000)))
for i in range(5):
timer.time_op('slice-rows', lambda: expr.evaluate(x[200:300, :].sum()))
timer.time_op('slice-cols', lambda: expr.evaluate(x[:, 200:300].sum()))
timer.time_op('slice-box',
lambda: expr.evaluate(x[200:300, 200:300].sum()))
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 precompute(self):
'''Precompute the most k similar items for each item.
After this funcion returns. 2 attributes will be created.
Attributes
------
top_k_similar_table : Numpy array of shape (N, k).
Records the most k similar scores between each items.
top_k_similar_indices : Numpy array of shape (N, k).
Records the indices of most k similar items for each item.
'''
M = self.rating_table.shape[0]
N = self.rating_table.shape[1]
self.similarity_table = expr.shuffle(
self.rating_table,
_similarity_mapper,
kw={
'item_norm':
self._get_norm_of_each_item(self.rating_table),
'step':
util.divup(self.rating_table.shape[1],
blob_ctx.get().num_workers)
},
shape_hint=(N, N))
# Release the memory for item_norm
top_k_similar_indices = expr.zeros((N, self.k), dtype=np.int)
# Find top-k similar items for each item.
# Store the similarity scores into table top_k_similar table.
# Store the indices of top k items into table top_k_similar_indices.
cost = np.prod(top_k_similar_indices.shape)
top_k_similar_table = expr.shuffle(self.similarity_table,
_select_most_k_similar_mapper,
kw={
'top_k_similar_indices':
top_k_similar_indices,
'k': self.k
},
shape_hint=(N, self.k),
cost_hint={
hash(top_k_similar_indices): {
'00': 0,
'01': cost,
'10': cost,
'11': cost
}
})
self.top_k_similar_table = top_k_similar_table.optimized().glom()
self.top_k_similar_indices = top_k_similar_indices.optimized().glom()
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.
"""
X = expr.force(X)
num_dim = X.shape[1]
labels = expr.zeros((X.shape[0],1), dtype=np.int, tile_hint=X.tile_shape())
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
})
_.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):
"""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 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 fit(data, labels, num_tiles, 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.
num_tiles(int): the total tiles of the training data.
T(int): max training iterations.
la(float): lambda parameter of this SVM model.
'''
w = None
m = data.shape[0] / num_tiles
alpha = expr.zeros((m * num_tiles, 1), dtype=np.float64, tile_hint=(m,1)).force()
for i in range(T):
new_weight = expr.ndarray((data.shape[1], 1), dtype=np.float64, reduce_fn=np.add, tile_hint=(data.shape[1], 1))
new_weight = expr.shuffle(data, _svm_mapper, target=new_weight, kw={'labels': labels, 'alpha': alpha, 'w': w, 'm': m, 'scale': num_tiles, 'lambda_n': la * data.shape[0]})
w = new_weight / num_tiles
return w
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, tile_hint=(k, num_dim))
labels = expr.zeros((points.shape[0],), dtype=np.int, tile_hint=(points.shape[0]/len(points.tiles),))
for iter in range(num_iter):
new_centers = expr.ndarray((k, num_dim), reduce_fn=lambda a, b: a + b, tile_hint=(k, num_dim))
new_counts = expr.ndarray((k, 1), dtype=np.float, reduce_fn=lambda a, b: a + b, tile_hint=(k, 1))
expr.shuffle(points, _fuzzy_kmeans_mapper, kw={'old_centers': centers,
'centers': new_centers,
'counts': new_counts,
'labels': labels,
'm': m}).force()
# If any centroids don't have any points assigned to them.
zcount_indices = (new_counts.glom() == 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.
new_counts[zcount_indices, :] = 1
new_centers[zcount_indices, :] = np.random.rand(np.count_nonzero(zcount_indices), num_dim)
centers = new_centers / new_counts
return labels
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 test_count_zero(self):
x = expr.ones((TEST_SIZE, ))
Assert.eq(expr.count_zero(x).glom(), 0)
x = expr.zeros((TEST_SIZE, ))
Assert.eq(expr.count_zero(x).glom(), TEST_SIZE)
def train_smo_1998(self, data, labels):
'''
Train an SVM model using the SMO (1998) algorithm.
Args:
data(Expr): points to be trained
labels(Expr): the correct labels of the training data
'''
N = data.shape[0] # Number of instances
D = data.shape[1] # Number of features
self.b = 0.0
self.alpha = expr.zeros((N, 1),
dtype=np.float64,
tile_hint=[N / self.ctx.num_workers,
1]).force()
# linear kernel
kernel_results = expr.dot(data,
expr.transpose(data),
tile_hint=[N / self.ctx.num_workers, N])
labels = expr.force(labels)
self.E = expr.zeros((N, 1),
dtype=np.float64,
tile_hint=[N / self.ctx.num_workers, 1]).force()
for i in xrange(N):
self.E[i, 0] = self.b + expr.reduce(
self.alpha,
axis=None,
dtype_fn=lambda input: input.dtype,
local_reduce_fn=margin_mapper,
accumulate_fn=np.add,
fn_kw=dict(
label=labels,
data=kernel_results[:, i].force())).glom() - labels[i, 0]
util.log_info("Starting SMO")
it = 0
num_changed = 0
examine_all = True
while (num_changed > 0 or examine_all) and (it < self.maxiter):
util.log_info("Iteration:%d", it)
num_changed = 0
if examine_all:
for i in xrange(N):
num_changed += self.examine_example(
i, N, labels, kernel_results)
else:
for i in xrange(N):
if self.alpha[i, 0] > 0 and self.alpha[i, 0] < self.C:
num_changed += self.examine_example(
i, N, labels, kernel_results)
it += 1
if examine_all: examine_all = False
elif num_changed == 0: examine_all = True
self.w = expr.zeros((D, 1), dtype=np.float64).force()
for i in xrange(D):
self.w[i, 0] = expr.reduce(self.alpha,
axis=None,
dtype_fn=lambda input: input.dtype,
local_reduce_fn=margin_mapper,
accumulate_fn=np.add,
fn_kw=dict(label=labels,
data=expr.force(
data[:, i]))).glom()
self.usew_ = True
print 'iteration finish:', it
print 'b:', self.b
print 'w:', self.w.glom()
def train_smo_2005(self, data, labels):
'''
Train an SVM model using the SMO (2005) algorithm.
Args:
data(Expr): points to be trained
labels(Expr): the correct labels of the training data
'''
N = data.shape[0] # Number of instances
D = data.shape[1] # Number of features
self.b = 0.0
alpha = expr.zeros((N,1), dtype=np.float64, tile_hint=[N/self.ctx.num_workers, 1]).force()
# linear kernel
kernel_results = expr.dot(data, expr.transpose(data), tile_hint=[N/self.ctx.num_workers, N])
gradient = expr.ones((N, 1), dtype=np.float64, tile_hint=[N/self.ctx.num_workers, 1]) * -1.0
expr_labels = expr.lazify(labels)
util.log_info("Starting SMO")
pv1 = pv2 = -1
it = 0
while it < self.maxiter:
util.log_info("Iteration:%d", it)
minObj = 1e100
expr_alpha = expr.lazify(alpha)
G = expr.multiply(labels, gradient) * -1.0
v1_mask = ((expr_labels > self.tol) * (expr_alpha < self.C) + (expr_labels < -self.tol) * (expr_alpha > self.tol))
v1 = expr.argmax(G[v1_mask-True]).glom().item()
maxG = G[v1,0].glom()
print 'maxv1:', v1, 'maxG:', maxG
v2_mask = ((expr_labels > self.tol) * (expr_alpha > self.tol) + (expr_labels < -self.tol) * (expr_alpha < self.C))
min_v2 = expr.argmin(G[v2_mask-True]).glom().item()
minG = G[min_v2,0].glom()
#print 'minv2:', min_v2, 'minG:', minG
set_v2 = v2_mask.glom().nonzero()[0]
#print 'actives:', set_v2.shape[0]
v2 = -1
for v in set_v2:
b = maxG - G[v,0].glom()
if b > self.tol:
na = (kernel_results[v1,v1] + kernel_results[v,v] - 2*kernel_results[v1,v]).glom()[0][0]
if na < self.tol: na = 1e12
obj = -(b*b)/na
if obj <= minObj and v1 != pv1 or v != pv2:
v2 = v
a = na
minObj = obj
if v2 == -1: break
if maxG - minG < self.tol: break
print 'opt v1:', v1, 'v2:', v2
pv1 = v1
pv2 = v2
y1 = labels[v1,0]
y2 = labels[v2,0]
oldA1 = alpha[v1,0]
oldA2 = alpha[v2,0]
# Calculate new alpha values, to reduce the objective function...
b = y2*expr.glom(gradient[v2,0]) - y1*expr.glom(gradient[v1,0])
if y1 != y2:
a += 4 * kernel_results[v1,v2].glom()
newA1 = oldA1 + y1*b/a
newA2 = oldA2 - y2*b/a
# Correct for alpha being out of range...
sum = y1*oldA1 + y2*oldA2;
if newA1 < self.tol: newA1 = 0.0
elif newA1 > self.C: newA1 = self.C
newA2 = y2 * (sum - y1 * newA1)
if newA2 < self.tol: newA2 = 0.0;
elif newA2 > self.C: newA2 = self.C
newA1 = y1 * (sum - y2 * newA2)
# Update the gradient...
dA1 = newA1 - oldA1
dA2 = newA2 - oldA2
gradient += expr.multiply(labels, kernel_results[:,v1]) * y1 * dA1 + expr.multiply(labels, kernel_results[:,v2]) * y2 * dA2
alpha[v1,0] = newA1
alpha[v2,0] = newA2
#print 'alpha:', alpha.glom().T
it += 1
#print 'gradient:', gradient.glom().T
self.w = expr.zeros((D, 1), dtype=np.float64).force()
for i in xrange(D):
self.w[i,0] = expr.reduce(alpha, axis=None, dtype_fn=lambda input: input.dtype,
local_reduce_fn=margin_mapper,
accumulate_fn=np.add,
fn_kw=dict(label=labels, data=expr.force(data[:,i]))).glom()
self.b = 0.0
E = (labels - self.margins(data)).force()
minB = -1e100
maxB = 1e100
actualB = 0.0
numActualB = 0
for i in xrange(N):
ai = alpha[i,0]
yi = labels[i,0]
Ei = E[i,0]
if ai < 1e-3:
if yi < self.tol:
maxB = min((maxB,Ei))
else:
minB = max((minB,Ei))
elif ai > self.C - 1e-3:
if yi < self.tol:
minB = max((minB,Ei))
else:
maxB = min((maxB,Ei))
else:
numActualB += 1
actualB += (Ei - actualB) / float(numActualB)
if numActualB > 0:
self.b = actualB
else:
self.b = 0.5*(minB + maxB)
self.usew_ = True
print 'iteration finish:', it
print 'b:', self.b
print 'w:', self.w.glom()
def train_smo_2005(self, data, labels):
'''
Train an SVM model using the SMO (2005) algorithm.
Args:
data(Expr): points to be trained
labels(Expr): the correct labels of the training data
'''
N = data.shape[0] # Number of instances
D = data.shape[1] # Number of features
self.b = 0.0
alpha = expr.zeros((N, 1),
dtype=np.float64,
tile_hint=[N / self.ctx.num_workers, 1]).force()
# linear kernel
kernel_results = expr.dot(data,
expr.transpose(data),
tile_hint=[N / self.ctx.num_workers, N])
gradient = expr.ones(
(N, 1), dtype=np.float64, tile_hint=[N / self.ctx.num_workers, 1
]) * -1.0
expr_labels = expr.lazify(labels)
util.log_info("Starting SMO")
pv1 = pv2 = -1
it = 0
while it < self.maxiter:
util.log_info("Iteration:%d", it)
minObj = 1e100
expr_alpha = expr.lazify(alpha)
G = expr.multiply(labels, gradient) * -1.0
v1_mask = ((expr_labels > self.tol) * (expr_alpha < self.C) +
(expr_labels < -self.tol) * (expr_alpha > self.tol))
v1 = expr.argmax(G[v1_mask - True]).glom().item()
maxG = G[v1, 0].glom()
print 'maxv1:', v1, 'maxG:', maxG
v2_mask = ((expr_labels > self.tol) * (expr_alpha > self.tol) +
(expr_labels < -self.tol) * (expr_alpha < self.C))
min_v2 = expr.argmin(G[v2_mask - True]).glom().item()
minG = G[min_v2, 0].glom()
#print 'minv2:', min_v2, 'minG:', minG
set_v2 = v2_mask.glom().nonzero()[0]
#print 'actives:', set_v2.shape[0]
v2 = -1
for v in set_v2:
b = maxG - G[v, 0].glom()
if b > self.tol:
na = (kernel_results[v1, v1] + kernel_results[v, v] -
2 * kernel_results[v1, v]).glom()[0][0]
if na < self.tol: na = 1e12
obj = -(b * b) / na
if obj <= minObj and v1 != pv1 or v != pv2:
v2 = v
a = na
minObj = obj
if v2 == -1: break
if maxG - minG < self.tol: break
print 'opt v1:', v1, 'v2:', v2
pv1 = v1
pv2 = v2
y1 = labels[v1, 0]
y2 = labels[v2, 0]
oldA1 = alpha[v1, 0]
oldA2 = alpha[v2, 0]
# Calculate new alpha values, to reduce the objective function...
b = y2 * expr.glom(gradient[v2, 0]) - y1 * expr.glom(gradient[v1,
0])
if y1 != y2:
a += 4 * kernel_results[v1, v2].glom()
newA1 = oldA1 + y1 * b / a
newA2 = oldA2 - y2 * b / a
# Correct for alpha being out of range...
sum = y1 * oldA1 + y2 * oldA2
if newA1 < self.tol: newA1 = 0.0
elif newA1 > self.C: newA1 = self.C
newA2 = y2 * (sum - y1 * newA1)
if newA2 < self.tol: newA2 = 0.0
elif newA2 > self.C: newA2 = self.C
newA1 = y1 * (sum - y2 * newA2)
# Update the gradient...
dA1 = newA1 - oldA1
dA2 = newA2 - oldA2
gradient += expr.multiply(
labels, kernel_results[:, v1]) * y1 * dA1 + expr.multiply(
labels, kernel_results[:, v2]) * y2 * dA2
alpha[v1, 0] = newA1
alpha[v2, 0] = newA2
#print 'alpha:', alpha.glom().T
it += 1
#print 'gradient:', gradient.glom().T
self.w = expr.zeros((D, 1), dtype=np.float64).force()
for i in xrange(D):
self.w[i, 0] = expr.reduce(alpha,
axis=None,
dtype_fn=lambda input: input.dtype,
local_reduce_fn=margin_mapper,
accumulate_fn=np.add,
fn_kw=dict(label=labels,
data=expr.force(
data[:, i]))).glom()
self.b = 0.0
E = (labels - self.margins(data)).force()
minB = -1e100
maxB = 1e100
actualB = 0.0
numActualB = 0
for i in xrange(N):
ai = alpha[i, 0]
yi = labels[i, 0]
Ei = E[i, 0]
if ai < 1e-3:
if yi < self.tol:
maxB = min((maxB, Ei))
else:
minB = max((minB, Ei))
elif ai > self.C - 1e-3:
if yi < self.tol:
minB = max((minB, Ei))
else:
maxB = min((maxB, Ei))
else:
numActualB += 1
actualB += (Ei - actualB) / float(numActualB)
if numActualB > 0:
self.b = actualB
else:
self.b = 0.5 * (minB + maxB)
self.usew_ = True
print 'iteration finish:', it
print 'b:', self.b
print 'w:', self.w.glom()
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 test_count_nonzero(self): x = expr.ones((TEST_SIZE,)) Assert.eq(expr.count_nonzero(x).glom(), TEST_SIZE) x = expr.zeros((TEST_SIZE,)) Assert.eq(expr.count_nonzero(x).glom(), 0)
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 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
