def test_operations():
a = np.ones((2, 3))
b = np.ones((2, 3))
c = a + b
d = -c
print(d)
e = np.sin(c**2).T
print(e)
f = np.maximum(a, c)
print(f)
def test_creation():
a = np.array([1, 2, 3])
print(a)
b = np.array([[1, 2, 3], [2, 3, 4]])
print(b)
a = np.zeros((2, 3))
print(a)
b = np.ones((2, 3))
print(b)
c = np.full((2, 3), 7)
print(c)
d = np.empty((2, 3))
print(d)
def gaussian_cluster_generator(num_samples=10000,
num_features=500,
num_classes=5):
mu = np.random.rand(num_classes, num_features)
sigma = np.ones((num_classes, num_features)) * 0.1
num_cls_samples = int(num_samples / num_classes)
x = np.zeros((num_samples, num_features))
y = np.zeros((num_samples, num_classes))
for i in range(num_classes):
cls_samples = np.random.normal(mu[i, :], sigma[i, :],
(num_cls_samples, num_features))
x[i * num_cls_samples:(i + 1) * num_cls_samples] = cls_samples
y[i * num_cls_samples:(i + 1) * num_cls_samples, i] = 1
return x, y
def constant(shape, config):
"""Initialize weights with constant value.
Parameters
----------
shape : tuple
Shape of the array to be initialized.
config : dict
The value to initailize the array
Returns
-------
Array
Initialized array of size `shape` and with the value `value`
"""
config.setdefault('value', 0.0)
val = config['value']
return np.ones(shape) * val
def test_NDArrayIter():
# check minpy.ndarray as input
datas = np.ones([1000, 2, 2])
labels = np.ones([1000, 1])
for i in range(1000):
datas[i] = i / 100
labels[i] = i / 100
dataiter = io.NDArrayIter(datas,
labels,
batch_size=100,
shuffle=True,
last_batch_handle='pad')
batchidx = 0
for batch in dataiter:
batchidx += 1
assert (batchidx == 10)
dataiter = io.NDArrayIter(datas,
labels,
batch_size=100,
shuffle=False,
last_batch_handle='pad')
batchidx = 0
labelcount = [0 for i in range(10)]
for batch in dataiter:
label = batch.label[0].asnumpy().flatten()
assert ((batch.data[0].asnumpy()[:, 0, 0] == label).all())
for i in range(label.shape[0]):
labelcount[int(label[i])] += 1
for i in range(10):
assert (labelcount[i] == 100)
# check numpy.ndarray as input
datas = ori_np.ones([1000, 2, 2])
labels = ori_np.ones([1000, 1])
for i in range(1000):
datas[i] = i / 100
labels[i] = i / 100
dataiter = io.NDArrayIter(datas,
labels,
batch_size=100,
shuffle=True,
last_batch_handle='pad')
batchidx = 0
for batch in dataiter:
batchidx += 1
assert (batchidx == 10)
dataiter = io.NDArrayIter(datas,
labels,
batch_size=100,
shuffle=False,
last_batch_handle='pad')
batchidx = 0
labelcount = [0 for i in range(10)]
for batch in dataiter:
label = batch.label[0].asnumpy().flatten()
assert ((batch.data[0][:, 0, 0].asnumpy() == label).all())
for i in range(label.shape[0]):
labelcount[int(label[i])] += 1
for i in range(10):
assert (labelcount[i] == 100)
# check padding
datas = np.ones([1000, 2, 2])
labels = np.ones([1000, 1])
for i in range(1000):
datas[i] = i / 100
labels[i] = i / 100
dataiter = io.NDArrayIter(datas,
labels,
batch_size=128,
shuffle=True,
last_batch_handle='pad')
batchidx = 0
for batch in dataiter:
batchidx += 1
assert (batchidx == 8)
dataiter = io.NDArrayIter(datas,
labels,
batch_size=128,
shuffle=False,
last_batch_handle='pad')
batchidx = 0
labelcount = [0 for i in range(10)]
for batch in dataiter:
label = batch.label[0].asnumpy().flatten()
assert ((batch.data[0].asnumpy()[:, 0, 0] == label).all())
for i in range(label.shape[0]):
labelcount[int(label[i])] += 1
for i in range(10):
if i == 0:
assert (labelcount[i] == 124)
else:
assert (labelcount[i] == 100)
# check padding
datas = ori_np.ones([1000, 2, 2])
labels = ori_np.ones([1000, 1])
for i in range(1000):
datas[i] = i / 100
labels[i] = i / 100
dataiter = io.NDArrayIter(datas,
labels,
batch_size=128,
shuffle=True,
last_batch_handle='pad')
batchidx = 0
for batch in dataiter:
batchidx += 1
assert (batchidx == 8)
dataiter = io.NDArrayIter(datas,
labels,
batch_size=128,
shuffle=False,
last_batch_handle='pad')
batchidx = 0
labelcount = [0 for i in range(10)]
for batch in dataiter:
label = batch.label[0].asnumpy().flatten()
assert ((batch.data[0].asnumpy()[:, 0, 0] == label).all())
for i in range(label.shape[0]):
labelcount[int(label[i])] += 1
for i in range(10):
if i == 0:
assert (labelcount[i] == 124)
else:
assert (labelcount[i] == 100)
def constant(shape, config):
config.setdefault('value', 0.0)
val = config['value']
return np.ones(shape) * val
def sample(self, features, max_length=30):
"""
Run a test-time forward pass for the model, sampling captions for input
feature vectors.
At each timestep, we embed the current word, pass it and the previous hidden
state to the RNN to get the next hidden state, use the hidden state to get
scores for all vocab words, and choose the word with the highest score as
the next word. The initial hidden state is computed by applying an affine
transform to the input image features, and the initial word is the <START>
token.
For LSTMs you will also have to keep track of the cell state; in that case
the initial cell state should be zero.
Inputs:
- features: Array of input image features of shape (N, D).
- max_length: Maximum length T of generated captions.
Returns:
- captions: Array of shape (N, max_length) giving sampled captions,
where each element is an integer in the range [0, V). The first element
of captions should be the first sampled word, not the <START> token.
"""
N = features.shape[0]
#captions = self._null * np.ones((N, max_length), dtype=np.int32)
captions = self._null * np.ones((N, max_length), dtype=int)
# Unpack parameters
W_proj, b_proj = self.params['W_proj'], self.params['b_proj']
W_embed = self.params['W_embed']
Wx, Wh, b = self.params['Wx'], self.params['Wh'], self.params['b']
W_vocab, b_vocab = self.params['W_vocab'], self.params['b_vocab']
h = affine_forward(features, W_proj, b_proj)
if self.cell_type == 'lstm':
c = np.zeros(h.shape)
embed = self._start * np.ones(N, dtype=int)
# TODO: Get captions
for t in xrange(max_length):
# (1) Embed the previous word using the learned word embeddings
# (2) Make an RNN / LSTM step using the previous hidden state and the
# embedded current word to get the next hidden state.
if self.cell_type == 'rnn':
h = rnn_step_forward(embed, h, Wx, Wh, b)
else:
h, c = lstm_step_forward(embed, h, c, Wx, Wh, b)
# (3) Apply the learned affine transformation to the next hidden state to
# get scores for all words in the vocabulary
# (4) Select the word with the highest score as the next word, writing it
# to the appropriate slot in the captions variable
# END TODO
############################################################################
# END OF YOUR CODE #
############################################################################
return captions
def MTNE(self):
# dictionary
D = np.random.rand(self.p, self.m)
lambda_ff_list = []
# Author
Aprime_list = []
# weight
W_list = []
# dense vector
F_list = []
# similarity local
X_list = []
X_mask_list = []
# all sparse embeddings across all timestamps
# F_big=np.zeros((self.q,self.k))
X_big = np.zeros((self.q, self.q))
X_mask_big = np.zeros((self.q, self.q))
S = np.random.rand(self.q, self.q)
indexDict_local2global = collections.OrderedDict()
indexDict_global2local = dict()
globalIndex = 0
for key in self.edgeDict:
A = self.edgeDict[key]
X = self.initX(A, self.theta)
X = X / (np.amax(X) - np.amin(X))
X_list.append(X)
X_mask = np.zeros((self.q, self.q))
# number of nodes in the current time
n = A.shape[0]
Aprime = np.random.rand(n, self.p)
Aprime_list.append(Aprime)
indexDict = dict()
for i in range(n):
indexDict[i] = globalIndex + i
indexDict_global2local[globalIndex + i] = (key, i)
indexDict_local2global[key] = indexDict
for i in range(n):
i_big = indexDict[i]
for j in range(n):
j_big = indexDict[j]
X_big[i_big, j_big] = X[i, j]
X_mask[i_big, j_big] = 1.
X_mask_big[i_big, j_big] = 1.
X_mask_list.append(X_mask)
globalIndex += n
W = np.random.rand(n, self.p)
W_list.append(W)
F = np.random.rand(n, self.m)
F_list.append(F)
lambda_ff_list.append(random.random())
F_big = self.concatenateMatrixInList(F_list, self.m, 0)
loss_t1 = 1000000000.0
print loss_t1
loss_t = self.epsilon + loss_t1 + 1
while abs(loss_t - loss_t1) >= self.epsilon:
#% optimize each element in randomized sequence
nita = 1. / math.sqrt(self.t)
self.t = self.t + 1
loss_t = loss_t1
loss_t1 = 0.0
counter = 0
for key in self.edgeDict:
X = X_list[counter]
W = W_list[counter]
F = F_list[counter]
Aprime = Aprime_list[counter]
indexDict = indexDict_local2global[key]
lambda_ff = lambda_ff_list[counter]
X_mask = X_mask_list[counter]
P = self.getP(X_mask, F_big, X_big)
n = X.shape[0]
for i in range(n):
# for A
z = Aprime[i] - nita * (
np.dot(np.dot(Aprime[i], W.T) - X[i], W) +
self.beta * np.dot(np.dot(Aprime[i], D) - F[i], D.T))
update = np.maximum(np.zeros(np.shape(z)),
np.abs(z) - nita * self.lamda_pgd)
Aprime[i] = np.sign(z) * update
# for F
lf_part1 = self.beta * F[i] - np.dot(Aprime[i], D)
lf_part2 = np.zeros(self.m)
i_big_index = indexDict[i]
for j in range(self.q):
lf_part2 += self.rho * (F[i] -
F_big[j]) * S[i_big_index, j]
val1 = np.dot(F[i], F_big.T)
val2 = val1 - np.ones(self.q)
val3 = np.dot(val2, F_big)
lf_part3 = 0.01 * val3
F[i] = F[i] - nita * (lf_part1 + lf_part2 + lf_part3)
F_big[i_big_index] = F[i]
# vec=np.dot(F[i],F_big.T)-np.ones(self.q)
# # print vec.shape
# lambda_ff=lambda_ff-nita*np.linalg.norm(vec)
# for S
ls = (S[i_big_index] -
P[i_big_index]) - self.epsilon * np.ones(
self.q) - self.alpha * S[i_big_index]
S[i_big_index] = S[i_big_index] - nita * ls
Aprime = self.chechnegtive(Aprime, None, None)
LW = np.dot((np.dot(W, Aprime.T) - X), Aprime) + self.lamda * W
W = W - nita * LW
W = self.chechnegtive(W, None, None)
p1 = np.dot(Aprime, D) - F
p2 = self.beta * np.dot(Aprime.T, p1)
LD = p2 + self.gamma * D
D = D - nita * LD
W_list[counter] = W
F_list[counter] = F
Aprime_list[counter] = Aprime
lambda_ff_list[counter] = lambda_ff
loss_t1_part = self.lossfuction(X, W, Aprime, F, D)
loss_t1 += loss_t1_part
counter += 1
# loss_last=self.laplacianLoss(simM,F)
simMD, simML = self.getLaplacian(S)
trval = np.trace(np.dot(np.dot(F_big.T, simML), F_big))
gapval = self.norm(X_mask_big * (S - X_big))
loss_t1 += self.rho * trval + self.eta * gapval
if loss_t < loss_t1 and loss_t != 0:
break
# print loss_t
print loss_t1
return [Aprime_list, F_list, S]
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_numeric():
# 'newaxis', 'ndarray', 'flatiter', 'nditer', 'nested_iters', 'ufunc',
# 'arange', 'array', 'zeros', 'count_nonzero', 'empty', 'broadcast',
# 'dtype', 'fromstring', 'fromfile', 'frombuffer', 'int_asbuffer',
# 'where', 'argwhere', 'copyto', 'concatenate', 'fastCopyAndTranspose',
# 'lexsort', 'set_numeric_ops', 'can_cast', 'promote_types',
# 'min_scalar_type', 'result_type', 'asarray', 'asanyarray',
# 'ascontiguousarray', 'asfortranarray', 'isfortran', 'empty_like',
# 'zeros_like', 'ones_like', 'correlate', 'convolve', 'inner', 'dot',
# 'einsum', 'outer', 'vdot', 'alterdot', 'restoredot', 'roll',
# 'rollaxis', 'moveaxis', 'cross', 'tensordot', 'array2string',
# 'get_printoptions', 'set_printoptions', 'array_repr', 'array_str',
# 'set_string_function', 'little_endian', 'require', 'fromiter',
# 'array_equal', 'array_equiv', 'indices', 'fromfunction', 'isclose', 'load',
# 'loads', 'isscalar', 'binary_repr', 'base_repr', 'ones', 'identity',
# 'allclose', 'compare_chararrays', 'putmask', 'seterr', 'geterr',
# 'setbufsize', 'getbufsize', 'seterrcall', 'geterrcall', 'errstate',
# 'flatnonzero', 'Inf', 'inf', 'infty', 'Infinity', 'nan', 'NaN', 'False_',
# 'True_', 'bitwise_not', 'full', 'full_like', 'matmul'
x = np.arange(6)
x = x.reshape((2, 3))
np.zeros_like(x)
y = np.arange(3, dtype=np.float)
np.zeros_like(y)
np.ones(5)
np.ones((5, ), dtype=np.int)
np.ones((2, 1))
s = (2, 2)
np.ones(s)
x = np.arange(6)
x = x.reshape((2, 3))
np.ones_like(x)
y = np.arange(3, dtype=np.float)
np.ones_like(y)
np.full((2, 2), np.inf)
x = np.arange(6, dtype=np.int)
np.full_like(x, 1)
np.full_like(x, 0.1)
np.full_like(y, 0.1)
np.count_nonzero(np.eye(4))
np.count_nonzero([[0, 1, 7, 0, 0], [3, 0, 0, 2, 19]])
np.count_nonzero([[0, 1, 7, 0, 0], [3, 0, 0, 2, 19]], axis=0)
np.count_nonzero([[0, 1, 7, 0, 0], [3, 0, 0, 2, 19]], axis=1)
a = [1, 2]
np.asarray(a)
a = np.array([1, 2])
np.asarray(a) is a
a = np.array([1, 2], dtype=np.float32)
np.asarray(a, dtype=np.float32) is a
np.asarray(a, dtype=np.float64) is a
np.asarray(a) is a
np.asanyarray(a) is a
a = [1, 2]
np.asanyarray(a)
np.asanyarray(a) is a
x = np.arange(6).reshape(2, 3)
np.ascontiguousarray(x, dtype=np.float32)
x = np.arange(6).reshape(2, 3)
y = np.asfortranarray(x)
x = np.arange(6).reshape(2, 3)
y = np.require(x, dtype=np.float32, requirements=['A', 'O', 'W', 'F'])
a = np.array([[1, 2, 3], [4, 5, 6]], order='C')
np.isfortran(a)
b = np.array([[1, 2, 3], [4, 5, 6]], order='FORTRAN')
np.isfortran(b)
a = np.array([[1, 2, 3], [4, 5, 6]], order='C')
np.isfortran(a)
b = a.T
np.isfortran(b)
np.isfortran(np.array([1, 2], order='FORTRAN'))
x = np.arange(6).reshape(2, 3)
np.argwhere(x > 1)
x = np.arange(-2, 3)
np.flatnonzero(x)
np.correlate([1, 2, 3], [0, 1, 0.5])
np.correlate([1, 2, 3], [0, 1, 0.5], "same")
np.correlate([1, 2, 3], [0, 1, 0.5], "full")
np.correlate([1 + 1j, 2, 3 - 1j], [0, 1, 0.5j], 'full')
np.correlate([0, 1, 0.5j], [1 + 1j, 2, 3 - 1j], 'full')
np.convolve([1, 2, 3], [0, 1, 0.5])
np.convolve([1, 2, 3], [0, 1, 0.5], 'same')
np.convolve([1, 2, 3], [0, 1, 0.5], 'valid')
rl = np.outer(np.ones((5, )), np.linspace(-2, 2, 5))
# im = np.outer(1j*np.linspace(2, -2, 5), np.ones((5,)))
# grid = rl + im
x = np.array(['a', 'b', 'c'], dtype=object)
np.outer(x, [1, 2, 3])
a = np.arange(60.).reshape(3, 4, 5)
b = np.arange(24.).reshape(4, 3, 2)
c = np.tensordot(a, b, axes=([1, 0], [0, 1]))
c.shape
# A slower but equivalent way of computing the same...
d = np.zeros((5, 2))
a = np.array(range(1, 9))
A = np.array(('a', 'b', 'c', 'd'), dtype=object)
x = np.arange(10)
np.roll(x, 2)
x2 = np.reshape(x, (2, 5))
np.roll(x2, 1)
np.roll(x2, 1, axis=0)
np.roll(x2, 1, axis=1)
a = np.ones((3, 4, 5, 6))
np.rollaxis(a, 3, 1).shape
np.rollaxis(a, 2).shape
np.rollaxis(a, 1, 4).shape
x = np.zeros((3, 4, 5))
np.moveaxis(x, 0, -1).shape
np.moveaxis(x, -1, 0).shape
np.transpose(x).shape
np.moveaxis(x, [0, 1], [-1, -2]).shape
np.moveaxis(x, [0, 1, 2], [-1, -2, -3]).shape
x = [1, 2, 3]
y = [4, 5, 6]
np.cross(x, y)
x = [1, 2]
y = [4, 5, 6]
np.cross(x, y)
x = [1, 2, 0]
y = [4, 5, 6]
np.cross(x, y)
x = [1, 2]
y = [4, 5]
np.cross(x, y)
x = np.array([[1, 2, 3], [4, 5, 6]])
y = np.array([[4, 5, 6], [1, 2, 3]])
np.cross(x, y)
np.cross(x, y, axisc=0)
x = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
y = np.array([[7, 8, 9], [4, 5, 6], [1, 2, 3]])
np.cross(x, y)
np.cross(x, y, axisa=0, axisb=0)
# np.array_repr(np.array([1,2]))
# np.array_repr(np.ma.array([0.]))
# np.array_repr(np.array([], np.int32))
x = np.array([1e-6, 4e-7, 2, 3])
# np.array_repr(x, precision=6, suppress_small=True)
# np.array_str(np.arange(3))
a = np.arange(10)
x = np.arange(4)
np.set_string_function(lambda x: 'random', repr=False)
grid = np.indices((2, 3))
grid.shape
grid[0] # row indices
grid[1] # column indices
x = np.arange(20).reshape(5, 4)
row, col = np.indices((2, 3))
x[row, col]
np.fromfunction(lambda i, j: i == j, (3, 3), dtype=int)
np.fromfunction(lambda i, j: i + j, (3, 3), dtype=int)
np.isscalar(3.1)
np.isscalar([3.1])
np.isscalar(False)
# np.binary_repr(3)
# np.binary_repr(-3)
# np.binary_repr(3, width=4)
# np.binary_repr(-3, width=3)
# np.binary_repr(-3, width=5)
# np.base_repr(5)
# np.base_repr(6, 5)
# np.base_repr(7, base=5, padding=3)
# np.base_repr(10, base=16)
# np.base_repr(32, base=16)
np.identity(3)
np.allclose([1e10, 1e-7], [1.00001e10, 1e-8])
np.allclose([1e10, 1e-8], [1.00001e10, 1e-9])
np.allclose([1e10, 1e-8], [1.0001e10, 1e-9])
# np.allclose([1.0, np.nan], [1.0, np.nan])
# np.allclose([1.0, np.nan], [1.0, np.nan], equal_nan=True)
np.isclose([1e10, 1e-7], [1.00001e10, 1e-8])
np.isclose([1e10, 1e-8], [1.00001e10, 1e-9])
np.isclose([1e10, 1e-8], [1.0001e10, 1e-9])
# np.isclose([1.0, np.nan], [1.0, np.nan])
# np.isclose([1.0, np.nan], [1.0, np.nan], equal_nan=True)
np.array_equal([1, 2], [1, 2])
np.array_equal(np.array([1, 2]), np.array([1, 2]))
np.array_equal([1, 2], [1, 2, 3])
np.array_equal([1, 2], [1, 4])
np.array_equiv([1, 2], [1, 2])
np.array_equiv([1, 2], [1, 3])
np.array_equiv([1, 2], [[1, 2], [1, 2]])
np.array_equiv([1, 2], [[1, 2, 1, 2], [1, 2, 1, 2]])
np.array_equiv([1, 2], [[1, 2], [1, 3]])
NDArrayIter(data[0], data[1]),
NDArrayIter(data[0], data[1]),
)
solver.init()
else:
model = builder.Model(network_in_network, 'softmax', (3, 32, 32,))
for arg, setting in model.param_configs.items():
print arg
shape = setting['shape']
if 'weight' in arg:
if len(shape) == 2:
n = shape[0]
elif len(shape) == 4:
n = np.prod(shape[1:])
else:
raise Exception()
std = (2 / float(n)) ** 0.5
model.params[arg] = np.random.normal(0, std, shape)
elif 'bias' in arg:
model.params[arg] = np.zeros(shape)
elif 'lower' in arg:
model.params[arg] = np.zeros(shape)
elif 'upper' in arg:
model.params[arg] = np.ones(shape)
else:
raise Exception()
parameters = {key : value.asnumpy() for key, value in model.params.items()}
output_file = 'NIN-%s-%s-initial-parameters' % (activation, ini_mode)
pickle.dump(parameters, open(output_file, 'wb'))
def activations(weights, *args):
cat_state = np.concatenate(args + (np.ones((args[0].shape[0], 1)), ),
axis=1)
return np.dot(cat_state, weights)
DEVICE = 0
DR_INTERVAL = 10
shapes = (1024, ) * 4 + (10, )
activation = getattr(builder, ACTIVATION)
set_context(gpu(DEVICE))
storage = {}
chd_list = []
mlp = builder.Sequential()
for i, shape in enumerate(shapes[:-1]):
mlp.append(builder.Affine(shape))
mlp.append(builder.Export('affine%d' % i, storage))
mlp.append(activation())
mlp.append(builder.Export('activation%d' % i, storage))
mlp.append(ChannelDivision(np.ones(shape)))
chd_list.append(mlp[-1])
mlp.append(builder.Export('chd%d' % i, storage))
mlp.append(builder.Affine(shapes[-1]))
model = builder.Model(mlp, 'softmax', (3072, ))
batch_size = 100
batches = len(data[0]) // batch_size
batch_index = 0
iterations = 25000
interval = 10
settings = {'learning_rate': 0.05}
initialize(model)
from data_iter import SyntheticData
import logging
a = mx.nd.array([1, 2, 3])
b = mx.nd.array([[1, 2, 3], [4, 5, 6]])
c = np.array([1, 2, 3])
d = np.array([[1, 2, 3], [4, 5, 6]])
c
a.context
a
a.size
a.dtype
mx.nd.array(numpy.array([1, 2, 3]))
type(numpy.array([1, 2, 3]))
type(c)
np.ones((2, 3))
np.ones([2, 3])
mx.nd.ones([2, 3])
mx.nd.ones([2, 3]).asnumpy()
def foo(x):
return (5 * (x**2) + 3 * x + 2)
print(foo(4))
d_foo = grad(foo)
d_l_foo = grad_and_loss(foo)
d_foo(4)
d_l_foo(4)
h = self._nonlinear(h)
self.previous_h.append(h)
Y0 = layers.relu(layers.affine(h, WY0, bias_Y0))
Y = layers.affine(Y0, WY, bias_Y)
return Y
def loss(self, prediction, Y):
return layers.softmax_loss(prediction, Y)
model = RNN()
# initialize model
for key, value in model.param_configs.items():
model.params[key] = getattr(initializers,
value['init_rule'])(value['shape'],
value.get(
'init_config', {}))
N = 64
X, Y = np.ones((N, 11, 128)), np.ones((N, ))
def loss_function(X, Y, *args):
predictions = model.forward(X, 'train')
return model.loss(predictions, Y)
gl = gradient_loss(loss_function, range(2, len(model.params) + 2))
g, loss = gl(X, Y, *list(model.params.values()))
def test_ufunc():
x = np.array([-1.2, 1.2])
np.absolute(x)
np.absolute(1.2 + 1j)
x = np.linspace(start=-10, stop=10, num=101)
np.add(1.0, 4.0)
x1 = np.arange(9.0).reshape((3, 3))
x2 = np.arange(3.0)
np.add(x1, x2)
np.arccos([1, -1])
x = np.linspace(-1, 1, num=100)
np.arccosh([np.e, 10.0])
np.arccosh(1)
np.arcsin(0)
np.arcsinh(np.array([np.e, 10.0]))
np.arctan([0, 1])
np.pi / 4
x = np.linspace(-10, 10)
x = np.array([-1, +1, +1, -1])
y = np.array([-1, -1, +1, +1])
np.arctan2(y, x) * 180 / np.pi
np.arctan2([1., -1.], [0., 0.])
np.arctan2([0., 0., np.inf], [+0., -0., np.inf])
np.arctanh([0, -0.5])
np.bitwise_and(13, 17)
np.bitwise_and(14, 13)
# np.binary_repr(12) return str
np.bitwise_and([14, 3], 13)
np.bitwise_and([11, 7], [4, 25])
np.bitwise_and(np.array([2, 5, 255]), np.array([3, 14, 16]))
np.bitwise_and([True, True], [False, True])
np.bitwise_or(13, 16)
# np.binary_repr(29)
np.bitwise_or(32, 2)
np.bitwise_or([33, 4], 1)
np.bitwise_or([33, 4], [1, 2])
np.bitwise_or(np.array([2, 5, 255]), np.array([4, 4, 4]))
# np.array([2, 5, 255]) | np.array([4, 4, 4])
np.bitwise_or(np.array([2, 5, 255, 2147483647], dtype=np.int32),
np.array([4, 4, 4, 2147483647], dtype=np.int32))
np.bitwise_or([True, True], [False, True])
np.bitwise_xor(13, 17)
# np.binary_repr(28)
np.bitwise_xor(31, 5)
np.bitwise_xor([31, 3], 5)
np.bitwise_xor([31, 3], [5, 6])
np.bitwise_xor([True, True], [False, True])
a = np.array([-1.7, -1.5, -0.2, 0.2, 1.5, 1.7, 2.0])
np.ceil(a)
a = np.array([-1.7, -1.5, -0.2, 0.2, 1.5, 1.7, 2.0])
np.trunc(a)
np.cos(np.array([0, np.pi / 2, np.pi]))
np.cosh(0)
x = np.linspace(-4, 4, 1000)
rad = np.arange(12.) * np.pi / 6
np.degrees(rad)
out = np.zeros((rad.shape))
r = np.degrees(rad, out)
# np.all(r == out) return bool
np.rad2deg(np.pi / 2)
np.divide(2.0, 4.0)
x1 = np.arange(9.0).reshape((3, 3))
x2 = np.arange(3.0)
np.divide(2, 4)
np.divide(2, 4.)
np.equal([0, 1, 3], np.arange(3))
np.equal(1, np.ones(1))
x = np.linspace(-2 * np.pi, 2 * np.pi, 100)
np.exp2([2, 3])
np.expm1(1e-10)
np.exp(1e-10) - 1
np.fabs(-1)
np.fabs([-1.2, 1.2])
a = np.array([-1.7, -1.5, -0.2, 0.2, 1.5, 1.7, 2.0])
np.floor(a)
np.floor_divide(7, 3)
np.floor_divide([1., 2., 3., 4.], 2.5)
np.fmod([-3, -2, -1, 1, 2, 3], 2)
np.remainder([-3, -2, -1, 1, 2, 3], 2)
np.fmod([5, 3], [2, 2.])
a = np.arange(-3, 3).reshape(3, 2)
np.fmod(a, [2, 2])
np.greater([4, 2], [2, 2])
a = np.array([4, 2])
b = np.array([2, 2])
a > b
np.greater_equal([4, 2, 1], [2, 2, 2])
np.hypot(3 * np.ones((3, 3)), 4 * np.ones((3, 3)))
np.hypot(3 * np.ones((3, 3)), [4])
np.bitwise_not is np.invert
np.invert(np.array([13], dtype=np.uint8))
# np.binary_repr(242, width=8)
np.invert(np.array([13], dtype=np.uint16))
np.invert(np.array([13], dtype=np.int8))
# np.binary_repr(-14, width=8)
np.invert(np.array([True, False]))
# np.isfinite(1)
# np.isfinite(0)
# np.isfinite(np.nan)
# np.isfinite(np.inf)
# np.isfinite(np.NINF)
x = np.array([-np.inf, 0., np.inf])
y = np.array([2, 2, 2])
np.isfinite(x, y)
# np.isinf(np.inf)
# np.isinf(np.nan)
# np.isinf(np.NINF)
# np.isinf([np.inf, -np.inf, 1.0, np.nan])
x = np.array([-np.inf, 0., np.inf])
y = np.array([2, 2, 2])
# np.isinf(x, y)
# np.isnan(np.nan)
# np.isnan(np.inf)
# np.binary_repr(5)
np.left_shift(5, 2)
# np.binary_repr(20)
np.left_shift(5, [1, 2, 3])
np.less([1, 2], [2, 2])
np.less_equal([4, 2, 1], [2, 2, 2])
x = np.array([0, 1, 2, 2**4])
xi = np.array([0 + 1.j, 1, 2 + 0.j, 4.j])
np.log2(xi)
prob1 = np.log(1e-50)
prob2 = np.log(2.5e-50)
prob12 = np.logaddexp(prob1, prob2)
prob12
np.exp(prob12)
prob1 = np.log2(1e-50)
prob2 = np.log2(2.5e-50)
prob12 = np.logaddexp2(prob1, prob2)
prob1, prob2, prob12
2**prob12
np.log1p(1e-99)
np.log(1 + 1e-99)
# np.logical_and(True, False)
# np.logical_and([True, False], [False, False])
x = np.arange(5)
# np.logical_and(x>1, x<4)
# np.logical_not(3)
# np.logical_not([True, False, 0, 1])
x = np.arange(5)
# np.logical_not(x<3)
# np.logical_or(True, False)
# np.logical_or([True, False], [False, False])
x = np.arange(5)
# np.logical_or(x < 1, x > 3)
# np.logical_xor(True, False)
# np.logical_xor([True, True, False, False], [True, False, True, False])
x = np.arange(5)
# np.logical_xor(x < 1, x > 3)
# np.logical_xor(0, np.eye(2))
np.maximum([2, 3, 4], [1, 5, 2])
# np.maximum([np.nan, 0, np.nan], [0, np.nan, np.nan])
# np.maximum(np.Inf, 1)
np.minimum([2, 3, 4], [1, 5, 2])
# np.minimum([np.nan, 0, np.nan],[0, np.nan, np.nan])
# np.minimum(-np.Inf, 1)
np.fmax([2, 3, 4], [1, 5, 2])
np.fmax(np.eye(2), [0.5, 2])
# np.fmax([np.nan, 0, np.nan],[0, np.nan, np.nan])
np.fmin([2, 3, 4], [1, 5, 2])
np.fmin(np.eye(2), [0.5, 2])
# np.fmin([np.nan, 0, np.nan],[0, np.nan, np.nan])
np.modf([0, 3.5])
np.modf(-0.5)
np.multiply(2.0, 4.0)
x1 = np.arange(9.0).reshape((3, 3))
x2 = np.arange(3.0)
np.multiply(x1, x2)
np.negative([1., -1.])
np.not_equal([1., 2.], [1., 3.])
np.not_equal([1, 2], [[1, 3], [1, 4]])
x1 = range(6)
np.power(x1, 3)
x2 = [1.0, 2.0, 3.0, 3.0, 2.0, 1.0]
np.power(x1, x2)
x2 = np.array([[1, 2, 3, 3, 2, 1], [1, 2, 3, 3, 2, 1]])
np.power(x1, x2)
deg = np.arange(12.) * 30.
np.radians(deg)
out = np.zeros((deg.shape))
ret = np.radians(deg, out)
ret is out
np.deg2rad(180)
np.reciprocal(2.)
np.reciprocal([1, 2., 3.33])
np.remainder([4, 7], [2, 3])
np.remainder(np.arange(7), 5)
# np.binary_repr(10)
np.right_shift(10, 1)
# np.binary_repr(5)
np.right_shift(10, [1, 2, 3])
a = np.array([-1.7, -1.5, -0.2, 0.2, 1.5, 1.7, 2.0])
np.rint(a)
np.sign([-5., 4.5])
np.sign(0)
# np.sign(5-2j)
# np.signbit(-1.2)
np.signbit(np.array([1, -2.3, 2.1]))
np.copysign(1.3, -1)
np.copysign([-1, 0, 1], -1.1)
np.copysign([-1, 0, 1], np.arange(3) - 1)
np.sin(np.pi / 2.)
np.sin(np.array((0., 30., 45., 60., 90.)) * np.pi / 180.)
x = np.linspace(-np.pi, np.pi, 201)
np.sinh(0)
# np.sinh(np.pi*1j/2)
np.sqrt([1, 4, 9])
np.sqrt([4, -1, -3 + 4J])
np.cbrt([1, 8, 27])
np.square([-1j, 1])
np.subtract(1.0, 4.0)
x1 = np.arange(9.0).reshape((3, 3))
x2 = np.arange(3.0)
np.subtract(x1, x2)
np.tan(np.array([-pi, pi / 2, pi]))
np.tanh((0, np.pi * 1j, np.pi * 1j / 2))
x = np.arange(5)
np.true_divide(x, 4)
x = np.arange(9)
y1, y2 = np.frexp(x)
y1 * 2**y2
np.ldexp(5, np.arange(4))
x = np.arange(6)
np.ldexp(*np.frexp(x))
def __init__(self,
hidden_dims,
input_dim=3 * 32 * 32,
num_classes=10,
dropout=0,
use_batchnorm=False,
reg=0.0,
weight_scale=1e-2,
seed=None,
dtype=py_np.float64,
conv_mode='lazy'):
"""
Initialize a new FullyConnectedNet.
Inputs:
- hidden_dims: A list of integers giving the size of each hidden layer.
- input_dim: An integer giving the size of the input.
- num_classes: An integer giving the number of classes to classify.
- dropout: Scalar between 0 and 1 giving dropout strength. If dropout=0 then
the network should not use dropout at all.
- use_batchnorm: Whether or not the network should use batch normalization.
- reg: Scalar giving L2 regularization strength.
- weight_scale: Scalar giving the standard deviation for random
initialization of the weights.
- seed: If not None, then pass this random seed to the dropout layers. This
will make the dropout layers deteriminstic so we can gradient check the
model.
"""
super(FullyConnectedNet, self).__init__(conv_mode)
self.use_batchnorm = use_batchnorm
self.use_dropout = dropout > 0
self.reg = reg
self.num_layers = 1 + len(hidden_dims)
self.params = {}
#Define parameter name given # layer
self.w_name = lambda l: 'W' + str(l)
self.b_name = lambda l: 'b' + str(l)
self.bn_ga_name = lambda l: 'bn_ga' + str(l)
self.bn_bt_name = lambda l: 'bn_bt' + str(l)
for l in range(self.num_layers):
if l == 0:
input_d = input_dim
else:
input_d = hidden_dims[l - 1]
if l < self.num_layers - 1:
out_d = hidden_dims[l]
else:
out_d = num_classes
self.params[self.w_name(l)] = random.randn(input_d,
out_d) * weight_scale
self.params[self.b_name(l)] = np.zeros((out_d))
if l < self.num_layers and self.use_batchnorm:
self.params[self.bn_ga_name(l)] = np.ones((out_d))
self.params[self.bn_bt_name(l)] = np.zeros((out_d))
self.param_keys = self.params.keys()
# When using dropout we need to pass a dropout_param dictionary to each
# dropout layer so that the layer knows the dropout probability and the mode
# (train / test). You can pass the same dropout_param to each dropout layer.
self.dropout_param = {}
if self.use_dropout:
self.dropout_param = {'mode': 'train', 'p': dropout}
if seed is not None:
self.dropout_param['seed'] = seed
# With batch normalization we need to keep track of running means and
# variances, so we need to pass a special bn_param object to each batch
# normalization layer.
self.bn_params = []
if self.use_batchnorm:
self.bn_params = [{
'mode': 'train'
} for i in xrange(self.num_layers - 1)]
# Build key's index in loss func's arglist
self.key_args_index = {}
for i, key in enumerate(self.param_keys):
# data, targets would be the first two elments in arglist
self.key_args_index[key] = self.data_target_cnt + i
# Init Key to index in loss_function args
self.w_idx = self.wrap_param_idx(self.w_name)
self.b_idx = self.wrap_param_idx(self.b_name)
self.bn_ga_idx = self.wrap_param_idx(self.bn_ga_name)
self.bn_bt_idx = self.wrap_param_idx(self.bn_bt_name)
molecularTrajectory.positionZ[-1, :]
]
#reaction simulation
if k_Matrix[0][1] > 0:
reactionTrajectory = reaction_3state(dt, totalTime, moleculeNum,
initState)
reactionTrajectory.react(k_Matrix, QA, QB, QC)
initState = reactionTrajectory.state[-1, :]
fluoreDonor = fluorescence_wzq(dt, Qfluor)
fluoreDonor.collectPhoton(molecularTrajectory.positionX,
molecularTrajectory.positionY,
molecularTrajectory.positionZ,
reactionTrajectory.state)
else:
state = np.ones([totalTime / dt, moleculeNum])
fluoreDonor = fluorescence_wzq(dt)
fluoreDonor.collectPhoton(molecularTrajectory.positionX,
molecularTrajectory.positionY,
molecularTrajectory.positionZ, state)
#collection fluorescence
#Donor channel
##Acceptor channel
#fluoreAcceptor = fluorescence(dt, Qfluor = Qacceptor)
#fluoreAcceptor.collectPhoton(molecularTrajectory.positionX, molecularTrajectory.positionY, molecularTrajectory.positionZ)
with open(path + '/donor_' + str(fileNum) + '.txt', 'a') as f:
np.savetxt(f, fluoreDonor.trace[:-10000], fmt='%.3f')
with open(path + '/donor_' + str(fileNum) + 'nr.txt', 'a') as f: