def train(net_shapes, net_params, optimizer, utility, pool):
# pass seed instead whole noise matrix to parallel will save your time
noise_seed = np.random.randint(
0, 2**32 - 1, size=N_KID,
dtype=np.uint32) #.repeat(2) # mirrored sampling
noise_seed = np.concatenate([noise_seed, noise_seed
]).reshape([2, N_KID]).T.reshape([N_KID * 2])
# serially train with GPU
rewards = []
for k_id in range(N_KID * 2):
reward = get_reward(net_shapes, net_params, env, CONFIG['ep_max_step'],
CONFIG['continuous_a'], [noise_seed[k_id], k_id])
rewards = np.array(rewards)
kids_rank = np.argsort(rewards)[::-1] # rank kid id by reward
cumulative_update = np.zeros_like(net_params) # initialize update values
for ui, k_id in enumerate(kids_rank):
np.random.seed(noise_seed[k_id]) # reconstruct noise using seed
cumulative_update += utility[ui] * sign(k_id) * np.random.randn(
net_params.size)
gradients = optimizer.get_gradients(cumulative_update /
(2 * N_KID * SIGMA))
return net_params + gradients, rewards
def eigenVectorAndEigenValue(self, M, k):
val, vec = np.linalg.eig(M)
sorted_indices = np.argsort(val)
topk_evecs = vec[:, sorted_indices[:k:1]]
topN = 0.0
counter = 0
for v in sorted_indices:
if counter == k: break
topN = topN + val[v]
counter += 1
return [topN, topk_evecs]
def test_fromnumeric():
# Functions
# 'alen', 'all', 'alltrue', 'amax', 'amin', 'any', 'argmax',
# 'argmin', 'argpartition', 'argsort', 'around', 'choose', 'clip',
# 'compress', 'cumprod', 'cumproduct', 'cumsum', 'diagonal', 'mean',
# 'ndim', 'nonzero', 'partition', 'prod', 'product', 'ptp', 'put',
# 'rank', 'ravel', 'repeat', 'reshape', 'resize', 'round_',
# 'searchsorted', 'shape', 'size', 'sometrue', 'sort', 'squeeze',
# 'std', 'sum', 'swapaxes', 'take', 'trace', 'transpose', 'var',
a = [4, 3, 5, 7, 6, 8]
indices = [0, 1, 4]
np.take(a, indices)
a = np.array(a)
# a[indices]
np.take(a, [[0, 1], [2, 3]])
a = np.zeros((10, 2))
b = a.T
a = np.arange(6).reshape((3, 2))
np.reshape(a, (2, 3)) # C-like index ordering
np.reshape(np.ravel(a), (2, 3)) # equivalent to C ravel then C reshape
np.reshape(a, (2, 3), order='F') # Fortran-like index ordering
np.reshape(np.ravel(a, order='F'), (2, 3), order='F')
a = np.array([[1, 2, 3], [4, 5, 6]])
np.reshape(a, 6)
np.reshape(a, 6, order='F')
np.reshape(a, (3, -1)) # the unspecified value is inferred to be 2
choices = [[0, 1, 2, 3], [10, 11, 12, 13], [20, 21, 22, 23],
[30, 31, 32, 33]]
np.choose([2, 3, 1, 0], choices)
np.choose([2, 4, 1, 0], choices, mode='clip') # 4 goes to 3 (4-1)
np.choose([2, 4, 1, 0], choices, mode='wrap') # 4 goes to (4 mod 4)
a = [[1, 0, 1], [0, 1, 0], [1, 0, 1]]
choices = [-10, 10]
np.choose(a, choices)
a = np.array([0, 1]).reshape((2, 1, 1))
c1 = np.array([1, 2, 3]).reshape((1, 3, 1))
c2 = np.array([-1, -2, -3, -4, -5]).reshape((1, 1, 5))
np.choose(a, (c1, c2)) # result is 2x3x5, res[0,:,:]=c1, res[1,:,:]=c2
np.repeat(3, 4)
x = np.array([[1, 2], [3, 4]])
np.repeat(x, 2)
np.repeat(x, 3, axis=1)
np.repeat(x, [1, 2], axis=0)
a = np.arange(5)
np.put(a, [0, 2], [-44, -55])
a = np.arange(5)
np.put(a, 22, -5, mode='clip')
x = np.array([[1, 2, 3]])
np.swapaxes(x, 0, 1)
x = np.array([[[0, 1], [2, 3]], [[4, 5], [6, 7]]])
np.swapaxes(x, 0, 2)
x = np.arange(4).reshape((2, 2))
np.transpose(x)
x = np.ones((1, 2, 3))
np.transpose(x, (1, 0, 2)).shape
a = np.array([3, 4, 2, 1])
np.partition(a, 3)
np.partition(a, (1, 3))
x = np.array([3, 4, 2, 1])
x[np.argpartition(x, 3)]
x[np.argpartition(x, (1, 3))]
x = [3, 4, 2, 1]
np.array(x)[np.argpartition(x, 3)]
a = np.array([[1, 4], [3, 1]])
np.sort(a) # sort along the last axis
np.sort(a, axis=None) # sort the flattened array
np.sort(a, axis=0) # sort along the first axis
dtype = [('name', 'S10'), ('height', float), ('age', int)]
values = [('Arthur', 1.8, 41), ('Lancelot', 1.9, 38), ('Galahad', 1.7, 38)]
a = np.array(values, dtype=dtype) # create a structured array
np.sort(a, order='height') # doctest: +SKIP
np.sort(a, order=['age', 'height']) # doctest: +SKIP
x = np.array([3, 1, 2])
np.argsort(x)
x = np.array([[0, 3], [2, 2]])
np.argsort(x, axis=0)
np.argsort(x, axis=1)
x = np.array([(1, 0), (0, 1)], dtype=[('x', '<i4'), ('y', '<i4')])
np.argsort(x, order=('x', 'y'))
np.argsort(x, order=('y', 'x'))
a = np.arange(6).reshape(2, 3)
np.argmax(a)
np.argmax(a, axis=0)
np.argmax(a, axis=1)
b = np.arange(6)
b[1] = 5
np.argmax(b) # Only the first occurrence is returned.
a = np.arange(6).reshape(2, 3)
np.argmin(a)
np.argmin(a, axis=0)
np.argmin(a, axis=1)
b = np.arange(6)
b[4] = 0
np.argmin(b) # Only the first occurrence is returned.
np.searchsorted([1, 2, 3, 4, 5], 3)
np.searchsorted([1, 2, 3, 4, 5], 3, side='right')
np.searchsorted([1, 2, 3, 4, 5], [-10, 10, 2, 3])
a = np.array([[0, 1], [2, 3]])
np.resize(a, (2, 3))
np.resize(a, (1, 4))
np.resize(a, (2, 4))
x = np.array([[[0], [1], [2]]])
x.shape
np.squeeze(x).shape
np.squeeze(x, axis=(2, )).shape
a = np.arange(4).reshape(2, 2)
a = np.arange(8).reshape(2, 2, 2)
a
a[:, :, 0] # main diagonal is [0 6]
a[:, :, 1] # main diagonal is [1 7]
np.trace(np.eye(3))
a = np.arange(8).reshape((2, 2, 2))
np.trace(a)
a = np.arange(24).reshape((2, 2, 2, 3))
np.trace(a).shape
x = np.array([[1, 2, 3], [4, 5, 6]])
np.ravel(x)
x.reshape(-1)
np.ravel(x, order='F')
np.ravel(x.T)
np.ravel(x.T, order='A')
a = np.arange(3)[::-1]
a
# a = np.arange(12).reshape(2,3,2).swapaxes(1,2); a
x = np.eye(3)
np.nonzero(x)
x[np.nonzero(x)]
np.transpose(np.nonzero(x))
a = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
a > 3
np.nonzero(a > 3)
np.shape(np.eye(3))
np.shape([[1, 2]])
np.shape([0])
np.shape(0)
a = np.array([(1, 2), (3, 4)], dtype=[('x', 'i4'), ('y', 'i4')])
np.shape(a)
a.shape
a = np.array([[1, 2], [3, 4], [5, 6]])
np.compress([0, 1], a, axis=0)
np.compress([False, True, True], a, axis=0)
np.compress([False, True], a, axis=1)
np.compress([False, True], a)
a = np.arange(10)
np.clip(a, 1, 8)
np.clip(a, 3, 6, out=a)
a = np.arange(10)
np.clip(a, [3, 4, 1, 1, 1, 4, 4, 4, 4, 4], 8)
np.sum([])
np.sum([0.5, 1.5])
np.sum([0.5, 0.7, 0.2, 1.5], dtype=np.int32)
np.sum([[0, 1], [0, 5]])
np.sum([[0, 1], [0, 5]], axis=0)
np.sum([[0, 1], [0, 5]], axis=1)
# np.ones(128, dtype=np.int8).sum(dtype=np.int8)
# np.any([[True, False], [True, True]])
# np.any([[True, False], [False, False]], axis=0)
# np.any([-1, 0, 5])
# np.any(np.nan)
# np.all([[True,False],[True,True]])
# np.all([[True,False],[True,True]], axis=0)
# np.all([-1, 4, 5])
# np.all([1.0, np.nan])
a = np.array([[1, 2, 3], [4, 5, 6]])
np.cumsum(a)
np.cumsum(a, dtype=float) # specifies type of output value(s)
np.cumsum(a, axis=0) # sum over rows for each of the 3 columns
np.cumsum(a, axis=1) # sum over columns for each of the 2 rows
x = np.arange(4).reshape((2, 2))
np.ptp(x, axis=0)
np.ptp(x, axis=1)
a = np.arange(4).reshape((2, 2))
np.amax(a) # Maximum of the flattened array
np.amax(a, axis=0) # Maxima along the first axis
np.amax(a, axis=1) # Maxima along the second axis
b = np.arange(5, dtype=np.float)
# b[2] = np.NaN
np.amax(b)
np.nanmax(b)
a = np.arange(4).reshape((2, 2))
np.amin(a) # Minimum of the flattened array
np.amin(a, axis=0) # Minima along the first axis
np.amin(a, axis=1) # Minima along the second axis
b = np.arange(5, dtype=np.float)
# b[2] = np.NaN
np.amin(b)
np.nanmin(b)
a = np.zeros((7, 4, 5))
a.shape[0]
np.alen(a)
x = np.array([536870910, 536870910, 536870910, 536870910])
np.prod(x) #random
np.prod([])
np.prod([1., 2.])
np.prod([[1., 2.], [3., 4.]])
np.prod([[1., 2.], [3., 4.]], axis=1)
x = np.array([1, 2, 3], dtype=np.uint8)
# np.prod(x).dtype == np.uint
x = np.array([1, 2, 3], dtype=np.int8)
# np.prod(x).dtype == np.int
a = np.array([1, 2, 3])
np.cumprod(a) # intermediate results 1, 1*2
a = np.array([[1, 2, 3], [4, 5, 6]])
np.cumprod(a, dtype=float) # specify type of output
np.cumprod(a, axis=0)
np.cumprod(a, axis=1)
np.ndim([[1, 2, 3], [4, 5, 6]])
np.ndim(np.array([[1, 2, 3], [4, 5, 6]]))
np.ndim(1)
a = np.array([[1, 2, 3], [4, 5, 6]])
np.size(a)
np.size(a, 1)
np.size(a, 0)
np.around([0.37, 1.64])
np.around([0.37, 1.64], decimals=1)
np.around([.5, 1.5, 2.5, 3.5, 4.5]) # rounds to nearest even value
np.around([1, 2, 3, 11], decimals=1) # ndarray of ints is returned
np.around([1, 2, 3, 11], decimals=-1)
a = np.array([[1, 2], [3, 4]])
np.mean(a)
np.mean(a, axis=0)
np.mean(a, axis=1)
a = np.zeros((2, 512 * 512), dtype=np.float32)
a[0, :] = 1.0
a[1, :] = 0.1
np.mean(a)
np.mean(a, dtype=np.float64)
a = np.array([[1, 2], [3, 4]])
np.std(a)
np.std(a, axis=0)
np.std(a, axis=1)
a = np.zeros((2, 512 * 512), dtype=np.float32)
a[0, :] = 1.0
a[1, :] = 0.1
np.std(a)
np.std(a, dtype=np.float64)
a = np.array([[1, 2], [3, 4]])
np.var(a)
np.var(a, axis=0)
np.var(a, axis=1)
a = np.zeros((2, 512 * 512), dtype=np.float32)
a[0, :] = 1.0
a[1, :] = 0.1
np.var(a)
np.var(a, dtype=np.float64)
def test_fromnumeric():
# Functions
# 'alen', 'all', 'alltrue', 'amax', 'amin', 'any', 'argmax',
# 'argmin', 'argpartition', 'argsort', 'around', 'choose', 'clip',
# 'compress', 'cumprod', 'cumproduct', 'cumsum', 'diagonal', 'mean',
# 'ndim', 'nonzero', 'partition', 'prod', 'product', 'ptp', 'put',
# 'rank', 'ravel', 'repeat', 'reshape', 'resize', 'round_',
# 'searchsorted', 'shape', 'size', 'sometrue', 'sort', 'squeeze',
# 'std', 'sum', 'swapaxes', 'take', 'trace', 'transpose', 'var',
a = [4, 3, 5, 7, 6, 8]
indices = [0, 1, 4]
np.take(a, indices)
a = np.array(a)
# a[indices]
np.take(a, [[0, 1], [2, 3]])
a = np.zeros((10, 2))
b = a.T
a = np.arange(6).reshape((3, 2))
np.reshape(a, (2, 3)) # C-like index ordering
np.reshape(np.ravel(a), (2, 3)) # equivalent to C ravel then C reshape
np.reshape(a, (2, 3), order='F') # Fortran-like index ordering
np.reshape(np.ravel(a, order='F'), (2, 3), order='F')
a = np.array([[1,2,3], [4,5,6]])
np.reshape(a, 6)
np.reshape(a, 6, order='F')
np.reshape(a, (3,-1)) # the unspecified value is inferred to be 2
choices = [[0, 1, 2, 3], [10, 11, 12, 13],
[20, 21, 22, 23], [30, 31, 32, 33]]
np.choose([2, 3, 1, 0], choices)
np.choose([2, 4, 1, 0], choices, mode='clip') # 4 goes to 3 (4-1)
np.choose([2, 4, 1, 0], choices, mode='wrap') # 4 goes to (4 mod 4)
a = [[1, 0, 1], [0, 1, 0], [1, 0, 1]]
choices = [-10, 10]
np.choose(a, choices)
a = np.array([0, 1]).reshape((2,1,1))
c1 = np.array([1, 2, 3]).reshape((1,3,1))
c2 = np.array([-1, -2, -3, -4, -5]).reshape((1,1,5))
np.choose(a, (c1, c2)) # result is 2x3x5, res[0,:,:]=c1, res[1,:,:]=c2
np.repeat(3, 4)
x = np.array([[1,2],[3,4]])
np.repeat(x, 2)
np.repeat(x, 3, axis=1)
np.repeat(x, [1, 2], axis=0)
a = np.arange(5)
np.put(a, [0, 2], [-44, -55])
a = np.arange(5)
np.put(a, 22, -5, mode='clip')
x = np.array([[1,2,3]])
np.swapaxes(x,0,1)
x = np.array([[[0,1],[2,3]],[[4,5],[6,7]]])
np.swapaxes(x,0,2)
x = np.arange(4).reshape((2,2))
np.transpose(x)
x = np.ones((1, 2, 3))
np.transpose(x, (1, 0, 2)).shape
a = np.array([3, 4, 2, 1])
np.partition(a, 3)
np.partition(a, (1, 3))
x = np.array([3, 4, 2, 1])
x[np.argpartition(x, 3)]
x[np.argpartition(x, (1, 3))]
x = [3, 4, 2, 1]
np.array(x)[np.argpartition(x, 3)]
a = np.array([[1,4],[3,1]])
np.sort(a) # sort along the last axis
np.sort(a, axis=None) # sort the flattened array
np.sort(a, axis=0) # sort along the first axis
dtype = [('name', 'S10'), ('height', float), ('age', int)]
values = [('Arthur', 1.8, 41), ('Lancelot', 1.9, 38),
('Galahad', 1.7, 38)]
a = np.array(values, dtype=dtype) # create a structured array
np.sort(a, order='height') # doctest: +SKIP
np.sort(a, order=['age', 'height']) # doctest: +SKIP
x = np.array([3, 1, 2])
np.argsort(x)
x = np.array([[0, 3], [2, 2]])
np.argsort(x, axis=0)
np.argsort(x, axis=1)
x = np.array([(1, 0), (0, 1)], dtype=[('x', '<i4'), ('y', '<i4')])
np.argsort(x, order=('x','y'))
np.argsort(x, order=('y','x'))
a = np.arange(6).reshape(2,3)
np.argmax(a)
np.argmax(a, axis=0)
np.argmax(a, axis=1)
b = np.arange(6)
b[1] = 5
np.argmax(b) # Only the first occurrence is returned.
a = np.arange(6).reshape(2,3)
np.argmin(a)
np.argmin(a, axis=0)
np.argmin(a, axis=1)
b = np.arange(6)
b[4] = 0
np.argmin(b) # Only the first occurrence is returned.
np.searchsorted([1,2,3,4,5], 3)
np.searchsorted([1,2,3,4,5], 3, side='right')
np.searchsorted([1,2,3,4,5], [-10, 10, 2, 3])
a=np.array([[0,1],[2,3]])
np.resize(a,(2,3))
np.resize(a,(1,4))
np.resize(a,(2,4))
x = np.array([[[0], [1], [2]]])
x.shape
np.squeeze(x).shape
np.squeeze(x, axis=(2,)).shape
a = np.arange(4).reshape(2,2)
a = np.arange(8).reshape(2,2,2); a
a[:,:,0] # main diagonal is [0 6]
a[:,:,1] # main diagonal is [1 7]
np.trace(np.eye(3))
a = np.arange(8).reshape((2,2,2))
np.trace(a)
a = np.arange(24).reshape((2,2,2,3))
np.trace(a).shape
x = np.array([[1, 2, 3], [4, 5, 6]])
np.ravel(x)
x.reshape(-1)
np.ravel(x, order='F')
np.ravel(x.T)
np.ravel(x.T, order='A')
a = np.arange(3)[::-1]; a
# a = np.arange(12).reshape(2,3,2).swapaxes(1,2); a
x = np.eye(3)
np.nonzero(x)
x[np.nonzero(x)]
np.transpose(np.nonzero(x))
a = np.array([[1,2,3],[4,5,6],[7,8,9]])
a > 3
np.nonzero(a > 3)
np.shape(np.eye(3))
np.shape([[1, 2]])
np.shape([0])
np.shape(0)
a = np.array([(1, 2), (3, 4)], dtype=[('x', 'i4'), ('y', 'i4')])
np.shape(a)
a.shape
a = np.array([[1, 2], [3, 4], [5, 6]])
np.compress([0, 1], a, axis=0)
np.compress([False, True, True], a, axis=0)
np.compress([False, True], a, axis=1)
np.compress([False, True], a)
a = np.arange(10)
np.clip(a, 1, 8)
np.clip(a, 3, 6, out=a)
a = np.arange(10)
np.clip(a, [3,4,1,1,1,4,4,4,4,4], 8)
np.sum([])
np.sum([0.5, 1.5])
np.sum([0.5, 0.7, 0.2, 1.5], dtype=np.int32)
np.sum([[0, 1], [0, 5]])
np.sum([[0, 1], [0, 5]], axis=0)
np.sum([[0, 1], [0, 5]], axis=1)
# np.ones(128, dtype=np.int8).sum(dtype=np.int8)
# np.any([[True, False], [True, True]])
# np.any([[True, False], [False, False]], axis=0)
# np.any([-1, 0, 5])
# np.any(np.nan)
# np.all([[True,False],[True,True]])
# np.all([[True,False],[True,True]], axis=0)
# np.all([-1, 4, 5])
# np.all([1.0, np.nan])
a = np.array([[1,2,3], [4,5,6]])
np.cumsum(a)
np.cumsum(a, dtype=float) # specifies type of output value(s)
np.cumsum(a,axis=0) # sum over rows for each of the 3 columns
np.cumsum(a,axis=1) # sum over columns for each of the 2 rows
x = np.arange(4).reshape((2,2))
np.ptp(x, axis=0)
np.ptp(x, axis=1)
a = np.arange(4).reshape((2,2))
np.amax(a) # Maximum of the flattened array
np.amax(a, axis=0) # Maxima along the first axis
np.amax(a, axis=1) # Maxima along the second axis
b = np.arange(5, dtype=np.float)
# b[2] = np.NaN
np.amax(b)
np.nanmax(b)
a = np.arange(4).reshape((2,2))
np.amin(a) # Minimum of the flattened array
np.amin(a, axis=0) # Minima along the first axis
np.amin(a, axis=1) # Minima along the second axis
b = np.arange(5, dtype=np.float)
# b[2] = np.NaN
np.amin(b)
np.nanmin(b)
a = np.zeros((7,4,5))
a.shape[0]
np.alen(a)
x = np.array([536870910, 536870910, 536870910, 536870910])
np.prod(x) #random
np.prod([])
np.prod([1.,2.])
np.prod([[1.,2.],[3.,4.]])
np.prod([[1.,2.],[3.,4.]], axis=1)
x = np.array([1, 2, 3], dtype=np.uint8)
# np.prod(x).dtype == np.uint
x = np.array([1, 2, 3], dtype=np.int8)
# np.prod(x).dtype == np.int
a = np.array([1,2,3])
np.cumprod(a) # intermediate results 1, 1*2
a = np.array([[1, 2, 3], [4, 5, 6]])
np.cumprod(a, dtype=float) # specify type of output
np.cumprod(a, axis=0)
np.cumprod(a,axis=1)
np.ndim([[1,2,3],[4,5,6]])
np.ndim(np.array([[1,2,3],[4,5,6]]))
np.ndim(1)
a = np.array([[1,2,3],[4,5,6]])
np.size(a)
np.size(a,1)
np.size(a,0)
np.around([0.37, 1.64])
np.around([0.37, 1.64], decimals=1)
np.around([.5, 1.5, 2.5, 3.5, 4.5]) # rounds to nearest even value
np.around([1,2,3,11], decimals=1) # ndarray of ints is returned
np.around([1,2,3,11], decimals=-1)
a = np.array([[1, 2], [3, 4]])
np.mean(a)
np.mean(a, axis=0)
np.mean(a, axis=1)
a = np.zeros((2, 512*512), dtype=np.float32)
a[0, :] = 1.0
a[1, :] = 0.1
np.mean(a)
np.mean(a, dtype=np.float64)
a = np.array([[1, 2], [3, 4]])
np.std(a)
np.std(a, axis=0)
np.std(a, axis=1)
a = np.zeros((2, 512*512), dtype=np.float32)
a[0, :] = 1.0
a[1, :] = 0.1
np.std(a)
np.std(a, dtype=np.float64)
a = np.array([[1, 2], [3, 4]])
np.var(a)
np.var(a, axis=0)
np.var(a, axis=1)
a = np.zeros((2, 512*512), dtype=np.float32)
a[0, :] = 1.0
a[1, :] = 0.1
np.var(a)
np.var(a, dtype=np.float64)
n = 500
with open('corpus.txt', 'r', encoding='utf-8') as fin:
for line in fin:
line = line.strip().split('\t')
if len(line) != 4:
continue
words = line[3]
words = [w for w in words.split(' ') if w not in stopwords]
if len(words) < 10:
continue
docs.append(words)
if n > 0:
n -= 1
else:
break
with gpu(0):
plsa = PLSA(docs, 25, 5)
for i,te, pe in plsa.run():
print('iter %d, te: %f, pe: %f' % (i, te, pe))
topic_words = np.argsort(plsa._psi_k_j)
for t, tw in enumerate(topic_words):
tw = tw[:20]
words = [plsa._vocab[int(i)] for i in tw]
print('topic %d: %s' % (t, ' '.join(words)))