def initialize(self, K):
"""Accepts K number of topics in document.
Initializes all of the hidden variable arrays now that it knows dimensions
of topics, vocabulary, etc.
"""
assert self.documents is not None
# give at least more documents than topics
# so that it's not singular
assert self.D > K
self.K = K
D = self.D
W = self.W
# "it suffices to fix alpha to uniform 1/K"
# initialize to ones so that the topics are more evenly distributed
# good for small datasets
self.alpha = np.ones((K,)) * (3.0 / K)
# Initialize the variational distribution q(beta|lambda)
self.beta = topiclib.initialize_beta(K, W)
document_Nds = self.num_words_per(self.documents)
self.phi = [(np.ones((document_Nds[d], K))*(1.0/K)) for d in xrange(D)]
self.gamma = np.ones((D, K)) * (1.0 / K)
graphlib.initialize_random(self.gamma)
self.is_initialized = True
def __init__(self, neuralLayerNum, numNodes, nextNeuralLayerNumNodes):
if nextNeuralLayerNumNodes != None:
self.numNodes = numNodes+1
self.isOutputLayer = False
self.transMat = np.ones([self.numNodes, nextNeuralLayerNumNodes+1], dtype=float)
self.rpropDelta = DEL_INIT * np.ones([self.numNodes, nextNeuralLayerNumNodes+1], dtype=float)
self.rpropDelEDelW = np.ones([self.numNodes, nextNeuralLayerNumNodes+1], dtype=float)
self.rpropEdgesDelta = np.zeros([self.numNodes, nextNeuralLayerNumNodes+1], dtype=float)
else:
#Output layer
self.numNodes = numNodes
self.isOutputLayer = True
self.transMat = None
self.rpropDelta = None
self.rpropDelEDelW = None
self.rpropEdgesDelta = None
self.neuralLayerNum = neuralLayerNum
self.nodesInput = np.zeros([self.numNodes], dtype=float)
self.nodesOutput = np.zeros([self.numNodes], dtype=float)
self.nodesDelta = np.zeros([self.numNodes], dtype=float)
#set the bias node output to 1. and it never changes
if not self.isOutputLayer:
self.nodesOutput[-1] = 1.
def update_link(self, i):
n = self.n
neis = self.graph.neighbors(i)
degs = np.array(self.graph.degree().values())
candidates = np.ones(n, dtype=int)
candidates[i] = 0
#candidates[neis] = 0
candidates[neis] = [0] * len(neis) # pypy
oi = self.opinions[i]
if self.types[i] == 0:
candidates[self.opinions[candidates] != oi] = 0
else:
candidates[self.opinions[candidates] == oi] = 0
#candidates = np.nonzero(candidates)[0]
candidates = [k for k in range(n) if candidates[k] == 1] # pypy
degs = degs[candidates]
if list(candidates):
#newnei = candidates[randint(len(candidates))]
newnei = candidates[randweight(degs/float(sum(degs)))];
if neis:
oldnei = neis[randint(len(neis))]
# if oldnei would become isolated don't do anything
if len(list(self.graph.edge[oldnei].keys())) <= 1:
return
self.graph.add_edge(i, newnei)
self.graph.remove_edge(i, oldnei)
def lda_recalculate_log_beta(text, log_beta, log_phi):
"""
update topics: βk,wnew ∝ ΣdΣn 1(wd,n = w) φkd,n
Accepts log beta matrix (KxW) and
log phi, a D-length list of (N x K) matrices.
"""
(K,W) = log_beta.shape
D = len(log_phi)
# todo: jperla: should use -inf or a different really small number?!
log_beta[:,:] = np.ones(log_beta.shape) * float('-1000')
if isinstance(text[0], np.ndarray):
for d in xrange(D):
for n,word in enumerate(text[d]):
for k in xrange(K):
log_beta[k,word] = np.logaddexp(log_beta[k,word], log_phi[d][n][k])
else:
for d in xrange(D):
for n,word,count in iterwords(text[d]):
for k in xrange(K):
log_beta[k,word] = np.logaddexp(log_beta[k,word], log_phi[d][n][k])
graphlib.log_row_normalize(log_beta)
return log_beta
def solve(data,size):
max_size=2**size
A = np.ones([max_size,size],float)*sys.float_info.max
A[0,0] = 0
for m in range(2,size+1):
subsets = it.combinations(range(1,size),m-1)
for s in subsets:
s = (0,)+s
v = get_value(s)
for j in s:
if 0 == j: continue
vmin = sys.float_info.max
v_tmp = get_value_without_v(s,j)
for k in s:
if k == j: continue
v_cand = A[v_tmp,k] + get_dist(data,k,j)
if v_cand < vmin:
vmin = v_cand
A[v,j] = vmin
rst = sys.float_info.max
v = get_value(range(size))
for j in range(size):
v_cand = A[v,j] + get_dist(data,j,0)
if v_cand < rst:
rst = v_cand
return rst
def test_sub_where(self):
from numpypy import where, ones, zeros, array
a = array([1, 2, 3, 0, -3])
v = a.view(self.NoNew)
b = where(array(v) > 0, ones(5), zeros(5))
assert (b == [1, 1, 1, 0, 0]).all()
# where returns an ndarray irregardless of the subtype of v
assert not isinstance(b, self.NoNew)
def calculate_EZZT_from_small_log_phis(phi1, phi2):
"""
Accepts a big phi matrix (like ((Nd+Nc) x (K+J))
Calculates E[ZdZdT].
Returns the final matrix ((K+J) x (K+J)).
(Also, E[ZdZdT] = (1/N2)(ΣNΣm!=nφd,nφd,mT + ΣNdiag{φd,n})
"""
Nd,K = phi1.shape
Nc,J = phi2.shape
(Ndc, KJ) = (Nd+Nc, K+J)
inner_sum = np.zeros((KJ, KJ))
p1 = np.matrix(phi1)
p2 = np.matrix(phi2)
for i in xrange(K):
for j in xrange(K):
m = logdotexp(np.matrix(p1[:,i]), np.matrix(p1[:,j]).T)
m += np.diagonal(np.ones(Nd) * -1000)
inner_sum[i,j] = logsumexp(m.flatten())
for i in xrange(J):
for j in xrange(J):
m = logdotexp(np.matrix(p2[:,i]), np.matrix(p2[:,j]).T)
m += np.diagonal(np.ones(Nc) * -1000)
inner_sum[K+i,K+j] = logsumexp(m.flatten())
for i in xrange(K):
for j in xrange(J):
m = logdotexp(np.matrix(p1[:,i]), np.matrix(p2[:,j]).T)
inner_sum[i,K+j] = logsumexp(m.flatten())
for i in xrange(J):
for j in xrange(K):
m = logdotexp(np.matrix(p2[:,i]), np.matrix(p1[:,j]).T)
inner_sum[K+i,j] = logsumexp(m.flatten())
big_phi_sum = np.concatenate((logsumexp(phi1, axis=0),
logsumexp(phi2, axis=0)), axis=1)
ensure(big_phi_sum.shape == (KJ,))
for i in xrange(KJ):
inner_sum[i,i] = logsumexp([inner_sum[i,i], big_phi_sum[i]])
inner_sum -= np.log(Ndc * Ndc)
return inner_sum
def test_ones(self):
from numpypy import ones
a = ones(3)
assert len(a) == 3
assert a[0] == 1
raises(IndexError, "a[3]")
a[2] = 4
assert a[2] == 4
def initialize_uniform(matrix):
"""Accepts a matrix with a defined shape.
Initializes it to to be uniform probability on row.
Each row on last dimension should sum to 1.
Returns the original matrix, modified.
"""
nrows,ncols = matrix.shape
matrix = np.ones(matrix.shape)*(1.0/ncols)
return matrix
def test_vstack(self):
import numpypy as np
a = np.array([1, 2, 3])
b = np.array([2, 3, 4])
c = np.vstack((a, b))
assert np.array_equal(c, [[1, 2, 3],
[2, 3, 4]])
a = np.array([[1], [2], [3]])
b = np.array([[2], [3], [4]])
c = np.vstack((a, b))
assert np.array_equal(c, [[1],
[2],
[3],
[2],
[3],
[4]])
for shape1, shape2 in [[(2, 1), (3, 1)],
[(2, 4), [3, 4]]]:
a, b = np.ones(shape1), np.ones(shape2)
assert np.all(np.vstack((a, b)) ==
np.ones((a.shape[0] + b.shape[0],
a.shape[1])))
#skip("https://bugs.pypy.org/issue1394")
for shape1, shape2 in [[(3, 2, 4), (7, 2, 4)],
[(0, 2, 7), (10, 2, 7)],
[(0, 2, 7), (0, 2, 7)]]:
a, b = np.ones(shape1), np.ones(shape2)
assert np.all(np.vstack((a, b)) ==
np.ones((a.shape[0] + b.shape[0],
a.shape[1],
a.shape[2])))
def test_hstack(self):
import numpypy as np
a = np.array((1, 2, 3))
b = np.array((2, 3, 4))
c = np.hstack((a, b))
assert np.array_equal(c, [1, 2, 3, 2, 3, 4])
a = np.array([[1], [2], [3]])
b = np.array([[2], [3], [4]])
c = np.hstack((a, b))
assert np.array_equal(c, [[1, 2],
[2, 3],
[3, 4]])
for shape1, shape2 in [[(1, 2), (1, 3)],
[(4, 2), (4, 3)]]:
a, b = np.ones(shape1), np.ones(shape2)
assert np.all(np.hstack((a, b)) ==
np.ones((a.shape[0],
a.shape[1] + b.shape[1])))
#skip("https://bugs.pypy.org/issue1394")
for shape1, shape2 in [[(2, 3, 4), (2, 7, 4)],
[(1, 4, 7), (1, 10, 7)],
[(1, 4, 7), (1, 0, 7)],
[(1, 0, 7), (1, 0, 7)]]:
a, b = np.ones(shape1), np.ones(shape2)
assert np.all(np.hstack((a, b)) ==
np.ones((a.shape[0],
a.shape[1] + b.shape[1],
a.shape[2])))
def test_dstack(self):
import numpypy as np
a = np.array((1, 2, 3))
b = np.array((2, 3, 4))
c = np.dstack((a, b))
assert np.array_equal(c, [[[1, 2], [2, 3], [3, 4]]])
a = np.array([[1], [2], [3]])
b = np.array([[2], [3], [4]])
c = np.dstack((a, b))
assert np.array_equal(c, [[[1, 2]], [[2, 3]], [[3, 4]]])
#skip("https://bugs.pypy.org/issue1394")
for shape1, shape2 in [[(4, 2, 3), (4, 2, 7)],
[(7, 2, 0), (7, 2, 10)],
[(7, 2, 0), (7, 2, 0)]]:
a, b = np.ones(shape1), np.ones(shape2)
assert np.all(np.dstack((a, b)) ==
np.ones((a.shape[0],
a.shape[1],
a.shape[2] + b.shape[2])))
for shape1, shape2 in [[(4, 2, 3, 5), (4, 2, 7, 5)],
[(7, 2, 0, 5), (7, 2, 10, 5)],
[(7, 2, 0, 5), (7, 2, 0, 5)]]:
a, b = np.ones(shape1), np.ones(shape2)
assert np.all(np.dstack((a, b)) ==
np.ones((a.shape[0],
a.shape[1],
a.shape[2] + b.shape[2],
a.shape[3])))
def test_hstack(self):
import numpypy as np
a = np.array((1, 2, 3))
b = np.array((2, 3, 4))
c = np.hstack((a, b))
assert np.array_equal(c, [1, 2, 3, 2, 3, 4])
a = np.array([[1], [2], [3]])
b = np.array([[2], [3], [4]])
c = np.hstack((a, b))
assert np.array_equal(c, [[1, 2],
[2, 3],
[3, 4]])
for shape1, shape2 in [[(1, 2), (1, 3)],
[(4, 2), (4, 3)]]:
a, b = np.ones(shape1), np.ones(shape2)
assert np.all(np.hstack((a, b)) ==
np.ones((a.shape[0],
a.shape[1] + b.shape[1])))
#skip("https://bugs.pypy.org/issue1394")
for shape1, shape2 in [[(2, 3, 4), (2, 7, 4)],
[(1, 4, 7), (1, 10, 7)],
[(1, 4, 7), (1, 0, 7)],
[(1, 0, 7), (1, 0, 7)]]:
a, b = np.ones(shape1), np.ones(shape2)
assert np.all(np.hstack((a, b)) ==
np.ones((a.shape[0],
a.shape[1] + b.shape[1],
a.shape[2])))
def test_vstack(self):
import numpypy as np
a = np.array([1, 2, 3])
b = np.array([2, 3, 4])
c = np.vstack((a, b))
assert np.array_equal(c, [[1, 2, 3],
[2, 3, 4]])
a = np.array([[1], [2], [3]])
b = np.array([[2], [3], [4]])
c = np.vstack((a, b))
assert np.array_equal(c, [[1],
[2],
[3],
[2],
[3],
[4]])
for shape1, shape2 in [[(2, 1), (3, 1)],
[(2, 4), [3, 4]]]:
a, b = np.ones(shape1), np.ones(shape2)
assert np.all(np.vstack((a, b)) ==
np.ones((a.shape[0] + b.shape[0],
a.shape[1])))
#skip("https://bugs.pypy.org/issue1394")
for shape1, shape2 in [[(3, 2, 4), (7, 2, 4)],
[(0, 2, 7), (10, 2, 7)],
[(0, 2, 7), (0, 2, 7)]]:
a, b = np.ones(shape1), np.ones(shape2)
assert np.all(np.vstack((a, b)) ==
np.ones((a.shape[0] + b.shape[0],
a.shape[1],
a.shape[2])))
def test_dstack(self):
import numpypy as np
a = np.array((1, 2, 3))
b = np.array((2, 3, 4))
c = np.dstack((a, b))
assert np.array_equal(c, [[[1, 2], [2, 3], [3, 4]]])
a = np.array([[1], [2], [3]])
b = np.array([[2], [3], [4]])
c = np.dstack((a, b))
assert np.array_equal(c, [[[1, 2]], [[2, 3]], [[3, 4]]])
#skip("https://bugs.pypy.org/issue1394")
for shape1, shape2 in [[(4, 2, 3), (4, 2, 7)], [(7, 2, 0), (7, 2, 10)],
[(7, 2, 0), (7, 2, 0)]]:
a, b = np.ones(shape1), np.ones(shape2)
assert np.all(
np.dstack((a, b)) == np.ones((a.shape[0], a.shape[1],
a.shape[2] + b.shape[2])))
for shape1, shape2 in [[(4, 2, 3, 5), (4, 2, 7, 5)],
[(7, 2, 0, 5), (7, 2, 10, 5)],
[(7, 2, 0, 5), (7, 2, 0, 5)]]:
a, b = np.ones(shape1), np.ones(shape2)
assert np.all(
np.dstack((a, b)) == np.ones((a.shape[0], a.shape[1],
a.shape[2] + b.shape[2],
a.shape[3])))
def calculate_big_log_phi(phi1, phi2):
""" Pretends that two separate sets of D phi matrices (Nd x K) each
are one big phi matrix.
This is needed for the latent model.
The trick is to make a big matrix with four quadrants.
The top left quadrant has the phi1 matrix, the bottom right has phi2.
The remaining two quadrants are filled with zeros.
"""
(n1, k1) = phi1.shape
(n2, k2) = phi2.shape
big_phi = np.ones((n1 + n2, k1 + k2)) * float('-1000')
big_phi[0:n1,0:k1] = phi1
big_phi[n1:n1+n2,k1:k1+k2] = phi2
return big_phi
def __init__(self, neuralLayerNum, numNodes, nextNeuralLayerNumNodes):
self.numNodes = numNodes
self.neuralLayerNum = neuralLayerNum
self.nodesInput = np.zeros([self.numNodes], dtype=float)
self.nodesOutput = np.zeros([self.numNodes], dtype=float)
self.nodesDelta = np.zeros([self.numNodes], dtype=float)
if nextNeuralLayerNumNodes != None:
self.isOutputLayer = False
self.transMat = np.ones([self.numNodes, nextNeuralLayerNumNodes], dtype=float)
else:
self.isOutputLayer = True
self.transMat = None
def dijkstra(graph,source):
n = len(graph.vertices)
sp = np.ones([n],'float')*sys.float_info.max
sp[source]=0
visited = np.zeros([n],'bool')
all_visited=False
while (not all_visited):
next_id = get_next(sp,visited,n)
if next_id >= 0:
v = graph.vertices[next_id]
for e in v.out_edges:
w = e.tail
sp[w.id] = min(sp[w.id],sp[next_id]+e.cost)
visited[next_id]=True
else:
all_visited = True
return sp
def bellman_ford(graph,source):
n = len(graph.vertices)
A = np.ones([n+1,n],'float')*sys.float_info.max
A[0,source] = 0
for i in range(1,n+1):
for v in graph.vertices:
a1 = A[i-1,v.id]
a2 = sys.float_info.max
for e in v.in_edges:
a3 = A[i-1,e.head.id] + e.cost
a2 = a3 if a3 < a2 else a2
A[i,v.id] = min(a1,a2)
has_neg_cycles = False
for i in range(n):
if not A[n-1,i] == A[n,i]:
has_neg_cycles = True
break
return A[n-1] if not has_neg_cycles else []
def test_multidim_ones(self):
from numpypy import ones
a = ones((1, 2, 3))
assert a[0, 1, 2] == 1.0
def initialize(self, Ku, Ks, Kb):
"""Accepts K number of topics in document.
Initializes all of the hidden variable arrays now that it knows dimensions
of topics, vocabulary, etc.
"""
assert self.documents is not None
assert Ku is not None
assert Ks is not None
assert Kb is not None
K = Ku + Ks + Kb
# give at least more documents than topics
# so that it's not singular
assert self.D > K
self.K = K
self.Ku = Ku
self.Ks = Ks
self.Kb = Kb
self.Kc = self.Ku + self.Ks
self.Kl = self.Ks + self.Kb
W = self.W
# Initialize the variational distribution q(beta|lambda)
self.beta = topiclib.initialize_beta(K, W)
# "it suffices to fix alpha to uniform 1/K"
# initialize to ones so that the topics are more evenly distributed
# good for small datasets
self.alphaU = np.ones((Ku,)) * (1.0 / Ku)
self.alphaS = np.ones((Ks,)) * (1.0 / Ks)
self.alphaB = np.ones((Kb,)) * (1.0 / Kb)
# todo: not using this yet
#self.alphaD = ...
def uniform_phi(Nds, size):
D = len(Nds)
return [(np.ones((Nds[d], size)) * (1.0 / size)) for d in xrange(D)]
document_Nds = self.num_words_per(self.documents)
self.phiD = uniform_phi(document_Nds, self.Ku)
comment_Nds = self.num_words_per(self.comments)
self.phiC = uniform_phi(comment_Nds, self.Kc)
labeled_Nds = self.num_words_per(self.labeled)
self.phiL = uniform_phi(labeled_Nds, self.Kl)
background_Nds = self.num_words_per(self.background)
self.phiB = uniform_phi(background_Nds, self.Kb)
self.num_document_words = sum(document_Nds)
self.num_comment_words = sum(comment_Nds)
self.num_labeled_words = sum(labeled_Nds)
self.num_background_words = sum(background_Nds)
biggest = float(max(self.num_document_words, self.num_comment_words,
self.num_labeled_words, self.num_background_words))
self.document_multiplier = biggest / self.num_document_words
self.comment_multiplier = biggest / self.num_comment_words
self.labeled_multiplier = biggest / self.num_labeled_words
self.background_multiplier = biggest / self.num_background_words
self.gammaD = np.ones((self.D, self.Ku)) * (1.0 / self.Ku)
self.gammaC = np.ones((self.D, self.Kc)) * (1.0 / self.Kc)
self.gammaL = np.ones((self.L, self.Kl)) * (1.0 / self.Kl)
self.gammaB = np.ones((self.B, self.Kb)) * (1.0 / self.Kb)
graphlib.initialize_random(self.gammaD)
graphlib.initialize_random(self.gammaC)
graphlib.initialize_random(self.gammaL)
graphlib.initialize_random(self.gammaB)
self.eta = graphlib.random_normal(0, 2.0, (Ks,))
self.sigma_squared = 0.5
print 'eta start: {0}'.format(self.eta)
self.is_initialized = True
def test_where(self):
from numpypy import where, ones, zeros, array
a = [1, 2, 3, 0, -3]
a = where(array(a) > 0, ones(5), zeros(5))
assert (a == [1, 1, 1, 0, 0]).all()
def test_where_invalidates(self):
from numpypy import where, ones, zeros, array
a = array([1, 2, 3, 0, -3])
b = where(a > 0, ones(5), zeros(5))
a[0] = 0
assert (b == [1, 1, 1, 0, 0]).all()
def test_where_differing_dtypes(self):
from numpypy import array, ones, zeros, where
a = [1, 2, 3, 0, -3]
a = where(array(a) > 0, ones(5, dtype=int), zeros(5, dtype=float))
assert (a == [1, 1, 1, 0, 0]).all()
def uniform_phi(Nds, size):
D = len(Nds)
return [(np.ones((Nds[d], size)) * (1.0 / size)) for d in xrange(D)]
def __run(score_task_knapsack, m_tasks, tasks, mac): n = len(tasks) Tu = [] Td = range(0, n) Pu = numpy.zeros(2, float) Z = 0 X = numpy.zeros(n, int) Tc = [] B = numpy.ones(2, float) P = numpy.zeros((n, 2), float) G = numpy.zeros(n, float) U = numpy.zeros(n, float) for x in range(0, n): P[x][0] = m_tasks[tasks[x]].CPU_usage / mac.free_CPU() P[x][1] = m_tasks[tasks[x]].mem_usage / mac.free_mem() keep_going = True cnt = math.sqrt(2) #w_cpu = 0.6 #w_mem = 1 - w_cpu #print "MAC = %d, ntasks = %d" % (mac.machine_ID, n) while keep_going : # step 2 del Tc Tc = [] for i in Td: if P[i][0] <= (1. - Pu[0]) and P[i][1] <= (1. - Pu[1]): Tc.append(i) #print "2" # step 3 # terminate if Tc = empty if len(Tc) == 0: keep_going = False else: # step 4 # (a) if (numpy.dot(Pu, Pu) == 0.): for i in Tc: d = sum(P[i]) G[i] = (score_task_knapsack(m_tasks[tasks[i]], mac) * cnt)/d # (b) else: mod_Pu = math.sqrt(numpy.dot(Pu, Pu)) E = numpy.array(Pu * (1./mod_Pu)) for i in Tc: d = numpy.dot(P[i], E) G[i] = score_task_knapsack(m_tasks[tasks[i]], mac) / d #print "4" # step 5 v_max = -1 i_max = 0 for i in Tc: if G[i] > v_max: v_max = G[i] i_max = i #print "5" # step 6 Tu.append(i_max) Td.remove(i_max) Pu = Pu + P[i_max] Z = Z + m_tasks[tasks[i_max]].CPU_usage #print "(%f, %f)" % (mac.capacity_CPU, mac.capacity_memory) #print Pu return Tu
def test_ones_long(self):
from numpypy import ones, longlong
a = ones(10, dtype=long)
for i in range(10):
assert isinstance(a[i], longlong)
assert a[1] == 1
def test_ones_bool(self):
from numpypy import ones, True_
a = ones(10, dtype=bool)
for i in range(10):
assert a[i] is True_
def add_bias(A):
return np.hstack(( np.ones((A.shape[0],1)), A )) # Add 1 as bias.
def add_bias(A):
return np.hstack((np.ones((A.shape[0], 1)), A)) # Add 1 as bias.
def test_broadcast_setslice(self):
from numpypy import zeros, ones
a = zeros((100, 100))
b = ones(100)
a[:, :] = b
assert a[13, 15] == 1
def test_broadcast_call2(self):
from numpypy import zeros, ones
a = zeros((4, 1, 5))
b = ones((4, 3, 5))
b[:] = (a + a)
assert (b == zeros((4, 3, 5))).all()
def pyloop(a, b, c):
N = len(a)
assert N == len(b) == len(c)
res = numpy.zeros(N)
for i in range(N):
res[i] = a[i] + b[i] * c[i]
return res
def c_loop(a, b, c):
return numpy.add(a, numpy.multiply(b, c))
a = numpy.zeros(10000000)
b = numpy.ones(10000000)
c = numpy.ones(10000000)
x = time.clock()
res1 = pyloop(a, b, c)
y = time.clock()
print 'pyloop: %.4f secs' % (y - x)
x = time.clock()
res2 = c_loop(a, b, c)
y = time.clock()
print 'c_loop: %.4f secs' % (y - x)
assert (res1 == res2).all()
