def runKnn():
# generate indecies for the folds
folds = KFold(len(allData), n_folds=5)
# this loops over training and prediction for each fold
for train_rows, test_rows in folds:
# these four lines split the data into training and testing sets
trainFeats = features.iloc[train_rows]
trainLabs = labels.iloc[train_rows]
testFeats = features.iloc[test_rows]
testLabs = labels.iloc[test_rows]
# train the learner
knn = neighbors.KNeighborsRegressor(5, "distance")
knn.fit(trainFeats, trainLabs)
# measure accuracy
predictions = knn.predict(testFeats)
predictions = ndarray.flatten(predictions)
targets = ndarray.flatten(testLabs.values)
diffs = targets - predictions
sse = 0
se = 0
denom = len(diffs)
for val in diffs:
if str(val) == "nan":
denom -= 1
continue
sse += (val * val)
se += abs(val)
print(se / denom)
print(sse / denom)
print ""
def update(self,dict_in):
if self.data == []
self.y = flatten(dict_in['y'])
self.x = flatten(dict_in['x'])
value = 10*log10((norm(y-x,2)^2)/(norm(x_n-x,2)^2))
self.data.append(value)
def runKnn(): # generate indecies for the folds folds = KFold(len(allData), n_folds=5) # this loops over training and prediction for each fold for train_rows, test_rows in folds: # these four lines split the data into training and testing sets trainFeats = features.iloc[train_rows] trainLabs = labels.iloc[train_rows] testFeats = features.iloc[test_rows] testLabs = labels.iloc[test_rows] # train the learner knn = neighbors.KNeighborsRegressor(5, "distance") knn.fit(trainFeats, trainLabs) # measure accuracy predictions = knn.predict(testFeats) predictions = ndarray.flatten(predictions) targets = ndarray.flatten(testLabs.values) diffs = targets - predictions sse = 0 se = 0 denom = len(diffs) for val in diffs: if str(val) == "nan": denom -= 1 continue sse += (val * val) se += abs(val) print ( se / denom ) print ( sse / denom ) print ""
def gpremise(goals, goalsn, p):
m, _, o1, p1 = goals.shape
_, _, o2, p2 = goalsn.shape
X = zeros((m, o1 * p1 + o2 * p2))
for i in range(m):
X[i] = concatenate(
(ndarray.flatten(goals[i][p]), ndarray.flatten(goalsn[i][p])))
return X
def cinput(ctxs, ctxsn, goals, goalsn):
m, n, o = ctxs.shape
_, p = ctxsn.shape
X = zeros((m, 2 * n * o + 2 * p))
for i in range(m):
X[i] = concatenate((ndarray.flatten(ctxs[i]), ctxsn[i], \
ndarray.flatten(goals[i]), goalsn[i]))
return X
def ginput(data, datan):
m, n, o = data.shape
_, p = datan.shape
X = zeros((m, n * o + p))
for i in range(m):
X[i] = concatenate((ndarray.flatten(data[i]), datan[i]))
return X
def prepexpt(fn = '../Problem_nonoise_v2_init.mat'):
# fn = '../PSI/Problem_nonoise_v1.mat'
# fn = '../../PSI/PRECOMP_nonoise_py.mat'
D = io.loadmat(fn, struct_as_record=False, squeeze_me=True)
rho = 1e-0
lambdaFk_nuc = 1e-10
lambdaFu_nuc = 1e0
lambdaFk_TF = 1e-10
lambdaFu_TF = 1e-10
obs = D['obs']
opts = D['opts']
GT = D['GT']
Sk = D['Sk']
Fk = D['Fk']
Su = D['Su']
Fu = D['Fu']
if len(Su.shape)<2:
Su = Su[:,None]
Fu = Fu[None,:]
if len(GT.Su.shape)<2:
GT.Su = GT.Su[:,None]
GT.Fu = GT.Fu[None,:]
masks = D['masks']
bw = GT.bw
mask = ndarray.flatten(bw) > 0
Nvoxk = sum(mask)
[Nvox, Nproj] = opts.P.shape
[Nproj, T] = obs.data_in.shape
#Sk0 = GT.seg[mask, :]
#Fk0 = GT.activity[:, :T]
print('Initializing variables...')
Y = obs.data_in
P0 = opts.P.T # transpose
#Nsk = groundtruth.seg.shape[1]
#Sk = 1e-3 * sp.rand(Nvoxk, Nsk) # +Sk0
Nsk = Sk.shape[1]
Nsu = Su.shape[1]
print '#Sk:', Nsk, '#Su:', Nsu
print 'Done initialization!'
return (Y,Sk,Fk,Su,Fu,GT, P0, mask, masks)
def prepexpt(fn = '../Problem_nonoise_v2_init.mat'):
# fn = '../PSI/Problem_nonoise_v1.mat'
# fn = '../../PSI/PRECOMP_nonoise_py.mat'
D = io.loadmat(fn, struct_as_record=False, squeeze_me=True)
rho = 1e-0
lambdaFk_nuc = 1e-10
lambdaFu_nuc = 1e0
lambdaFk_TF = 1e-10
lambdaFu_TF = 1e-10
obs = D['obs']
opts = D['opts']
GT = D['GT']
Sk = D['Sk']
Fk = D['Fk']
Su = D['Su']
Fu = D['Fu']
if len(Su.shape)<2:
Su = Su[:,None]
Fu = Fu[None,:]
if len(GT.Su.shape)<2:
GT.Su = GT.Su[:,None]
GT.Fu = GT.Fu[None,:]
masks = D['masks']
bw = GT.bw
mask = ndarray.flatten(bw) > 0
Nvoxk = sum(mask)
[Nvox, Nproj] = opts.P.shape
[Nproj, T] = obs.data_in.shape
#Sk0 = GT.seg[mask, :]
#Fk0 = GT.activity[:, :T]
print('Initializing variables...')
Y = obs.data_in
P0 = opts.P.T # transpose
#Nsk = groundtruth.seg.shape[1]
#Sk = 1e-3 * sp.rand(Nvoxk, Nsk) # +Sk0
Nsk = Sk.shape[1]
Nsu = Su.shape[1]
print '#Sk:', Nsk, '#Su:', Nsu
print 'Done initialization!'
return (Y,Sk,Fk,Su,Fu,GT, P0, mask, masks)
def __init__(self, data):
"""
Constructor
@param data: Array (list or numpy array)
"""
# Create the CDF of the data
# sort the data:
if type(data) is pd.DataFrame:
self.arr = sort(ndarray.flatten(data.values))
else:
# self.arr = sort(ndarray.flatten(data), axis=0)
self.arr = sort(data, axis=0)
self.iscomplex = iscomplexobj(self.arr)
# calculate the proportional values of samples
n = len(data)
if n > 1:
self.prob = arange(n, dtype=float) / (n - 1)
else:
self.prob = arange(n, dtype=float)
# iterator index
self.idx = 0
# array length
self.len = len(self.arr)
def __get_tensor_from_ndarray(ndarray: numpy_types) -> JavaObject:
ctor = Tensor.__infer_tensor_ctor_from_ndarray(ndarray)
values = k.to_java_array(ndarray.flatten().tolist())
shape = k.to_java_long_array(ndarray.shape)
return ctor(values, shape)
def NGLostManN_f(NYrs, NGPctManApp, GrazingAnimal_0, NumAnimals, AvgAnimalWt, AnimalDailyN, NGAppNRate, Prec, DaysMonth,
NGPctSoilIncRate):
lossFactAdj = LossFactAdj_f(Prec, DaysMonth)
ng_app_man_n = NGAppManN_f(NGPctManApp, GrazingAnimal_0, NumAnimals, AvgAnimalWt, AnimalDailyN)
result = tile(ng_app_man_n * NGAppNRate * (1 - NGPctSoilIncRate), NYrs) * ndarray.flatten(lossFactAdj)
result = minimum(result, tile(ng_app_man_n, NYrs)) # TODO: should eliminate the double tile
result = maximum(result, 0)
return reshape(result, (NYrs, 12))
def plotN2d(self, sigMul=3, nxPts=20, nyPts=20):
sigma = 1. / np.sqrt(np.diag(self.invCov))
xa = np.linspace(self.mu[0] - sigMul * sigma[0],
self.mu[0] + sigMul * sigma[0], nxPts)
ya = np.linspace(self.mu[1] - sigMul * sigma[1],
self.mu[1] + sigMul * sigma[1], nyPts)
(xGrid, yGrid) = np.meshgrid(xa, ya)
xGridF = ndarray.flatten(xGrid)
yGridF = ndarray.flatten(yGrid)
zGridF = np.zeros_like(xGridF)
for (i, x) in enumerate(xGridF):
y = yGridF[i]
zGridF[i] = self.pdf(x, y)
zGrid = np.reshape(zGridF, (nxPts, nyPts))
plt.contour(xGrid, yGrid, zGrid, colors=['black'])
def vlad_vector(vector_path, vocabulary):
features = read_feature_vector(vector_path)
codes = vq(features, vocabulary)[0]
vlad = zeros(vocabulary.shape)
for idx in range(codes.size):
diff = subtract(features[idx], vocabulary[codes[idx]])
vlad[codes[idx]] = add(diff, vlad[codes[idx]])
return ndarray.flatten(vlad)
def GRLostManN_f(NYrs, GRPctManApp, GrazingAnimal_0, NumAnimals, AvgAnimalWt, AnimalDailyN, GRAppNRate, Prec, DaysMonth,
GRPctSoilIncRate):
lossFactAdj = LossFactAdj_f(Prec, DaysMonth)
gr_app_man_n = GRAppManN_f(GRPctManApp, GrazingAnimal_0, NumAnimals, AvgAnimalWt, AnimalDailyN)
result = (tile(gr_app_man_n, NYrs) * tile(GRAppNRate, NYrs) * ndarray.flatten(lossFactAdj) * tile(
(1 - GRPctSoilIncRate), NYrs))
result = minimum(result, tile(gr_app_man_n, NYrs))
result = maximum(result, 0)
return reshape(result, (NYrs, 12))
def get_most_frequent_val(data_array):
# Data array jest dwu-wymiarowa - trzeba ją "spłaszczyć" do tablicy 1-wymiarowej, aby móc wyznaczyć dominantę
flatten_array = ndarray.flatten(data_array)
converted_array = []
# Dane w "spłaszczonej" tablicy mogą mieć różne typy - trzeba je zrzutować na jeden typ
for i in flatten_array:
converted_array.append(int(i))
print("Wartość najczęściej występująca w zbiorze (dominanta): " +
str(bincount(converted_array).argmax()))
def vlad_vector(vector_path, vocabulary):
features = read_feature_vector(vector_path)
codes_idx = vq(features, vocabulary)[
0] #A length M array holding the code book index for each observation
vlad = zeros(vocabulary.shape)
for i in range(codes_idx.size):
code_idx = codes_idx[i]
diff = subtract(features[i], vocabulary[code_idx])
vlad[code_idx] = add(diff, vlad[code_idx])
return ndarray.flatten(vlad)
def embedPayload(self, payload, override=False):
if type(payload) != Payload:
raise TypeError
if payload.img.size > self.img.size:
raise ValueError
if self.payloadExists() and override == False:
raise Exception
xml = ndarray.flatten(
numpy.unpackbits(numpy.fromstring(payload.xml, dtype='uint8')))
img = self.img
if self.getScale() == "Color":
crimg = []
for i in img:
for j in i:
crimg.append(j[0])
for i in img:
for j in i:
crimg.append(j[1])
for i in img:
for j in i:
crimg.append(j[2])
crimg = ndarray.flatten(numpy.array(crimg))
crimg[0:xml.size] &= 254
crimg[0:xml.size] += xml
spl = lambda lst, sz: [
lst[i:i + sz] for i in range(0, len(lst), sz)
]
lst = spl(crimg, int(len(crimg) / 3))
l = []
for i in range(int(len(crimg) / 3)):
l.append([lst[0][i], lst[1][i], lst[2][i]])
img = numpy.array(l)
psize = self.img.shape
img = numpy.resize(img, (int(psize[0]), int(psize[1]), 3))
else:
img = ndarray.flatten(img)
img[0:xml.size] &= 254
img[0:xml.size] += xml
psize = self.img.shape
img = numpy.resize(img, psize)
return img
def GRLostManN_f(NYrs, GRPctManApp, GrazingAnimal_0, NumAnimals, AvgAnimalWt,
AnimalDailyN, GRAppNRate, Prec, DaysMonth, GRPctSoilIncRate):
lossFactAdj = LossFactAdj_f(Prec, DaysMonth)
gr_app_man_n = GRAppManN_f(GRPctManApp, GrazingAnimal_0, NumAnimals,
AvgAnimalWt, AnimalDailyN)
result = (tile(gr_app_man_n, NYrs) * tile(GRAppNRate, NYrs) *
ndarray.flatten(lossFactAdj) * tile(
(1 - GRPctSoilIncRate), NYrs))
result = minimum(result, tile(gr_app_man_n, NYrs))
result = maximum(result, 0)
return reshape(result, (NYrs, 12))
def __get_java_array_from_ndarray(ndarray: numpy_types) -> JavaObject:
if ndarray.size == 0:
raise ValueError("Cannot infer type because array is empty")
if np.issubdtype(ndarray.dtype, np.bool_):
return k.jvm_view().Py4jByteArrayConverter.toBooleanArray(
np.packbits(ndarray.flatten()).tobytes(), ndarray.size)
elif np.issubdtype(ndarray.dtype, np.int32):
return k.jvm_view().Py4jByteArrayConverter.toIntegerArray(
ndarray.flatten().tobytes())
elif np.issubdtype(ndarray.dtype, np.int64):
return k.jvm_view().Py4jByteArrayConverter.toLongArray(
ndarray.flatten().tobytes())
elif np.issubdtype(ndarray.dtype, np.floating):
return k.jvm_view().Py4jByteArrayConverter.toDoubleArray(
ndarray.flatten().tobytes())
else:
raise NotImplementedError(
"Generic types in an ndarray are not supported. Was given {}".
format(ndarray.dtype))
def NGLostManN_f(NYrs, NGPctManApp, GrazingAnimal_0, NumAnimals, AvgAnimalWt,
AnimalDailyN, NGAppNRate, Prec, DaysMonth, NGPctSoilIncRate):
lossFactAdj = LossFactAdj_f(Prec, DaysMonth)
ng_app_man_n = NGAppManN_f(NGPctManApp, GrazingAnimal_0, NumAnimals,
AvgAnimalWt, AnimalDailyN)
result = tile(ng_app_man_n * NGAppNRate *
(1 - NGPctSoilIncRate), NYrs) * ndarray.flatten(lossFactAdj)
result = minimum(result,
tile(ng_app_man_n,
NYrs)) # TODO: should eliminate the double tile
result = maximum(result, 0)
return reshape(result, (NYrs, 12))
def codePic(self):
if self.getScale() == "Gray":
gray = ndarray.flatten(self.img)
if self.cmplvl != -1:
gray = compress(ndarray.flatten(self.img), self.cmplvl)
imgCode = base64.b64encode(gray)
elif self.getScale() == "Color":
color = []
for i in self.img:
for j in i:
color.append(j[0])
for i in self.img:
for j in i:
color.append(j[1])
for i in self.img:
for j in i:
color.append(j[2])
color = numpy.array(color)
if self.cmplvl != -1:
color = compress(color, self.cmplvl)
imgCode = base64.b64encode(color)
return str(imgCode)[2:-1]
def runRegressionModel(model):
# generate indecies for the folds
folds = KFold(len(allData), n_folds=5)
curFold = 1
# this loops over training and prediction for each fold
for train_rows, test_rows in folds:
# these four lines split the data into training and testing sets
trainFeats = features.iloc[train_rows].as_matrix()
trainLabs = labels.iloc[train_rows].as_matrix()
testFeats = features.iloc[test_rows].as_matrix()
testLabs = labels.iloc[test_rows].as_matrix()
# train the learner
model = model.fit(trainFeats, trainLabs)
# measure accuracy
predictions = model.predict(testFeats)
predictions = ndarray.flatten(predictions)
targets = ndarray.flatten(testLabs)
diffs = targets - predictions
sse = 0
se = 0
denom = len(diffs)
for val in diffs:
if str(val) == "nan":
denom -= 1
continue
sse += (val * val)
se += abs(val)
print "== Fold " + str(curFold) + " =="
print "-- ME: " + str(se / denom)
print "-- MSE: " + str(sse / denom)
print ""
curFold += 1
def runRegressionModel(model): # generate indecies for the folds folds = KFold(len(allData), n_folds=5) curFold = 1 # this loops over training and prediction for each fold for train_rows, test_rows in folds: # these four lines split the data into training and testing sets trainFeats = features.iloc[train_rows].as_matrix() trainLabs = labels.iloc[train_rows].as_matrix() testFeats = features.iloc[test_rows].as_matrix() testLabs = labels.iloc[test_rows].as_matrix() # train the learner model = model.fit(trainFeats, trainLabs) # measure accuracy predictions = model.predict(testFeats) predictions = ndarray.flatten(predictions) targets = ndarray.flatten(testLabs) diffs = targets - predictions sse = 0 se = 0 denom = len(diffs) for val in diffs: if str(val) == "nan": denom -= 1 continue sse += (val * val) se += abs(val) print "== Fold " + str(curFold) + " ==" print "-- ME: " + str( se / denom ) print "-- MSE: " + str( sse / denom ) print "" curFold += 1
def sum_path(mat):
""" Finds the path with the minimal sum in a given matrix, where movement is only right and below"""
global sols
if size(mat) == 0:
return 0
if size(mat) == 1:
return mat[0,0]
if min(mat.shape[0],mat.shape[1]) == 1:
return sum(ndarray.flatten(mat))
if mat.shape in sols:
return sols[mat.shape]
ans = mat[-1,-1] + min(sum_path(mat[:,:-1]), sum_path(mat[:-1,:]))
sols[mat.shape] = ans
return ans
def compile_types(Sbus, types=None, logger=list()):
"""
Compile the types
@return:
"""
pq = where(types == BusMode.PQ.value[0])[0]
pv = where(types == BusMode.PV.value[0])[0]
ref = where(types == BusMode.REF.value[0])[0]
sto = where(types == BusMode.STO_DISPATCH.value)[0]
if len(ref) == 0: # there is no slack!
if len(pv) == 0: # there are no pv neither -> blackout grid
warn('There are no slack nodes selected')
logger.append('There are no slack nodes selected')
else: # select the first PV generator as the slack
mx = max(Sbus[pv])
if mx > 0:
# find the generator that is injecting the most
i = where(Sbus == mx)[0][0]
else:
# all the generators are injecting zero, pick the first pv
i = pv[0]
# delete the selected pv bus from the pv list and put it in the slack list
pv = delete(pv, where(pv == i)[0])
ref = [i]
# print('Setting bus', i, 'as slack')
ref = ndarray.flatten(array(ref))
types[ref] = BusMode.REF.value[0]
else:
pass # no problem :)
pqpv = r_[pq, pv]
pqpv.sort()
return ref, pq, pv, pqpv
def xbee_1():
device = XBeeDevice('COM4', 230400)
remote_addr = '0013A2004155E2A6'
remote_device = RemoteXBeeDevice(device, XBee64BitAddress.from_hex_string(remote_addr))
device.open()
msg = "Test from COM4"
count = 0
try:
rcv_bytes = device.read_data(1)
rcv = pickle.loads(rcv_bytes.data)
result = []
print('First rcv: {0:}'.format(rcv))
print('rcv.data_type: {0:}'.format(rcv.data_type))
if rcv.data_type == 'audio':
print('Got "Audio"')
result = []
result.append(rcv)
for i in range(0, rcv.total_sequence - 1):
rcv_bytes = device.read_data(1)
rcv = pickle.loads(rcv_bytes.data)
result.append(rcv)
print("COM4 received:" + repr(rcv))
result_arr = build_data(result)
print(result_arr)
flat_arr = ndarray.flatten(result_arr)
print(flat_arr)
newrecording = AudioSegment(flat_arr, sample_width=2, frame_rate=8000, channels=1)
newrecording.export('file.flac', format = "flac")
print('Sending from 4')
bytes_obj = pickle.dumps('ACK')
device.send_data(remote_device, bytes_obj)
except TimeoutException:
print("Timeout on COM4")
def extractPayload(self):
if self.payloadExists() is False:
raise Exception
if self.getScale() == "Gray":
new_img = self.img & 1
num = numpy.packbits(ndarray.flatten(new_img))
else:
color = []
for i in self.img:
for j in i:
color.append(j[0])
for i in self.img:
for j in i:
color.append(j[1])
for i in self.img:
for j in i:
color.append(j[2])
color = numpy.array(color) & 1
num = numpy.packbits(numpy.array(color))
xml = ''
for i in num:
xml += chr(i)
return Payload(xml=xml)
def payloadExists(self):
if self.getScale() == "Gray":
new_img = self.img & 1
num = numpy.packbits(ndarray.flatten(new_img))
else:
color = []
for i in self.img:
for j in i:
color.append(j[0])
for i in self.img:
for j in i:
color.append(j[1])
for i in self.img:
for j in i:
color.append(j[2])
color = numpy.array(color) & 1
num = numpy.packbits(numpy.array(color))
xml = ''
for i in num:
xml += chr(i)
if re.search(r'<?xml version', xml):
return True
else:
return False
def lossfun_aug():
l_aug = 0
# primary loss: the KL divergence between Y and X
l_Poiss = Y[:, tidx2] * (np.log(Y[:, tidx2]) - np.log(X[:, tidx2]))
l_Poiss = sum(l_Poiss[np.isfinite(l_Poiss)]) + sum(sum(X[:, tidx2] - Y[:, tidx2]))
l_X = (rho / 2) * sum(sum((Xhat[:, tidx2] - X[:, tidx2] + np.square(U_X[:, tidx2]) - np.square(U_X[:, tidx2]))))
l_Fk = 0
[u, s, v] = np.linalg.svd(Fk_nuc)
l_Fk = l_Fk + lambdaFk_nuc * sum(s)
l_Fk = l_Fk + (rho / 2) * sum(
(ndarray.flatten(Fk_nuc) - ndarray.flatten(Fk) + np.square(ndarray.flatten(U_Fk_nuc)) - np.square(ndarray.flatten(U_Fk_nuc))))
l_Fk = l_Fk + (rho / 2) * sum(
(ndarray.flatten(Fk_nn) - ndarray.flatten(Fk) + np.square(ndarray.flatten(U_Fk_nn)) - np.square(ndarray.flatten(U_Fk_nn))))
# l_Fk = l_Fk + lambdaFk_TF*sum(Fk_TF*)
# l_Fk = l_Fk + lambdaFk_TF*sum(Fk_TF*)
l_Fu = 0
[u, s, v] = np.linalg.svd(Fu_nuc)
l_Fu = l_Fu + lambdaFu_nuc * sum(s)
l_Fu = l_Fu + (rho / 2) * sum(
(ndarray.flatten(Fu_nuc) - ndarray.flatten(Fu) + np.square(ndarray.flatten(U_Fu_nuc)) - np.square(ndarray.flatten(U_Fu_nuc))))
l_Fu = l_Fu + (rho / 2) * sum(
(ndarray.flatten(Fu_nn) - ndarray.flatten(Fu) + np.square(ndarray.flatten(U_Fu_nn)) - np.square(ndarray.flatten(U_Fu_nn))))
l_Sk = 0
l_Sk = l_Sk + (rho / 2) * sum(
(ndarray.flatten(Sk_nn) - ndarray.flatten(Sk) + np.square(ndarray.flatten(U_Sk_nn)) - np.square(ndarray.flatten(U_Sk_nn))))
l_Su = 0
l_Su = l_Su + (rho / 2) * sum(
(ndarray.flatten(Su_nn) - ndarray.flatten(Su) + np.square(ndarray.flatten(U_Su_nn)) - np.square(ndarray.flatten(U_Su_nn))))
l_aug = l_Poiss + l_X + l_Fk + l_Fu + l_Sk + l_Su
print 'l_Poiss: ', l_Poiss
print 'l_X: ', l_X
print 'l_Fk: ', l_Fk
print 'l_Fu: ', l_Fu
print 'l_Sk: ', l_Sk
print 'l_Su: ', l_Su
l_aug = l_aug / Y[:, tidx2].size
return l_aug
async def pull_CB(self, instance, async_lib):
'Periodically update the value'
from time import time, sleep
await instance.write(value=ndarray.flatten(daq.buffer.get_all()))
def transfer_learning(self, max_iterations, accepted_mean_square_error=0.1, batch_size=5000, learning_rate=1e-4):
# Adam optimizer optimizes the parameters (weights and biases) at the learning rate specified
output_network_optimizer = Adam(self.output_neural_net.parameters(), lr=learning_rate)
# This is the row count difference between that of input and output grids
row_difference = self.output_grid_dimension[0] - self.input_grid_dimension[0]
# This is the column count difference between that of input and output grids
column_difference = self.output_grid_dimension[1] - self.input_grid_dimension[1]
# We cycle through iterations of each batch of training the output neural network until the max iteration
for _ in range(0, max_iterations):
# Training list - each element in the list contains [input state for output neural net, target value]
training_list = []
# Shuffling through batches and then calculating the Mean square error for the entire batch
for batch in range(0, batch_size):
# Creating a matrix to hold the observation state of the input neural network map (0 or 1)
output_network_known_state = np_array([[randint(0, 1) for _ in range(0, self.output_grid_dimension[1])]
for _ in range(0, self.output_grid_dimension[0])])
# This is used to store the robot and target state of the input neural network
input_network_state = np_empty((self.network_robot_count+1)*2, dtype=np_uint8)
# Creating positions for all of the robots and target of the input neural network randomly
for i in range(0, (self.network_robot_count+1)*2, 2):
input_network_state[i] = randint(0, self.input_grid_dimension[0]-1)
input_network_state[i+1] = randint(0, self.input_grid_dimension[1]-1)
# Creates a backup copy of the input state
input_network_state_memory = input_network_state
# Sliding the input network state window over different sections of the output network state
for i in range(0, row_difference):
for j in range(0, column_difference):
# Creating a matrix to hold the observation state of the input neural network map (0 or 1)
input_network_known_state = output_network_known_state[i:(i+self.input_grid_dimension[0]),
j:(j+self.input_grid_dimension[1])]
# This is used to store the robot and target state of the output neural network
output_network_state = np_empty((self.network_robot_count+1)*2, dtype=np_uint8)
# Extending the input position states across the moving window within the output grid dimensions
for k in range(0, (self.network_robot_count+1)*2, 2):
output_network_state[k] = input_network_state[k]+i
output_network_state[k+1] = input_network_state[k+1]+j
# Now we flatten data in the input network state, a 2-D matrix to 1-D and append
input_network_state = np_append(input_network_state,
np_ndarray.flatten(input_network_known_state))
# Now we flatten data in the output network state, a 2-D matrix to 1-D and append
output_network_state = np_append(output_network_state,
np_ndarray.flatten(output_network_known_state))
# Looping through the 4 possible actions each robot can take and then appending them to state
for k in range(0, 4):
# Adding an action completes the input state for the input neural network
input_network_state_tensor = Tensor(np_append(input_network_state, k))
# Adding an action completes the input state for the output neural network
output_network_state_tensor = Tensor(np_append(output_network_state, k))
# Getting the Q value predicted by the input neural network for the given state
input_network_predicted_value = self.input_neural_net.forward(input_network_state_tensor)
# Now we know the value the output neural network is to be trained towards for its given
# input. Add both of them to the training list so that batch training can occur later
training_list.append([output_network_state_tensor, input_network_predicted_value])
# Restoring the input state from memory
input_network_state = input_network_state_memory
# Shuffling the training data before feeding it in for training
shuffle(training_list)
# Initializing the current MSE loss
sum_square_error = 0.0
# Using the batch of state and target data for training the output neural network
for batch in range(0, batch_size):
# Obtaining the completed input states for the output neural network
output_network_state_tensor = training_list[batch][0]
# Obtaining the target predictions that the output neural network should be trained towards
predicted_target_value = training_list[batch][1]
# Getting the Q value predicted by the output neural network for the given input state
output_network_predicted_value = self.output_neural_net.forward(output_network_state_tensor)
# Adding the current square error to the sum of square errors
sum_square_error += pow((output_network_predicted_value - predicted_target_value), 2)
# Represents the function that can calculate training error
training_error_function = MSELoss()
# Our goal is to reduce the mean square error loss between the target prediction and that of network
training_error = training_error_function(output_network_predicted_value, predicted_target_value)
# Clears the gradients of all optimized torch tensors
output_network_optimizer.zero_grad()
# During the backwards pass, gradients from each replica are summed into the original module
training_error.backward()
# Training actually happens here. Performs a single optimization step of weights and biases
output_network_optimizer.step()
# Dividing the sum of square errors by the batch size to get the mean square error
current_mean_square_error = sum_square_error/batch_size
print(current_mean_square_error)
# Checks if the MSE for the entire batch is within acceptable levels and then returns the output neural net
if current_mean_square_error <= accepted_mean_square_error:
# we return a list where true indicates that we achieved the accepted mean square error criteria
return [self.output_neural_net, True]
# Failed to completely train the output neural network. Return a list with second element false to indicate this
return [self.output_neural_net, False]
def linear_symbolic(A=None, b=None, G=None, h=None):
"""Convert linear equality and inequality constraints from matrices to a
symbolic string of the form required by mystic's constraint parser.
Inputs:
A -- (ndarray) matrix of coefficients of linear equality constraints
b -- (ndarray) vector of solutions of linear equality constraints
G -- (ndarray) matrix of coefficients of linear inequality constraints
h -- (ndarray) vector of solutions of linear inequality constraints
NOTE: Must provide A and b; G and h; or A, b, G, and h;
where Ax = b and Gx <= h.
For example:
>>> A = [[3., 4., 5.],
... [1., 6., -9.]]
>>> b = [0., 0.]
>>> G = [1., 0., 0.]
>>> h = [5.]
>>> print linear_symbolic(A,b,G,h)
1.0*x0 + 0.0*x1 + 0.0*x2 <= 5.0
3.0*x0 + 4.0*x1 + 5.0*x2 = 0.0
1.0*x0 + 6.0*x1 + -9.0*x2 = 0.0
"""
eqstring = ""
# Equality constraints
if A != None and b != None:
# If one-dimensional and not in a nested list, add a list layer
try:
ndim = len(A[0])
except:
ndim = len(A)
A = [A]
# Flatten b, in case it's in the form [[0, 1, 2]] for example.
if len(b) == 1:
b = list(ndarray.flatten(asarray(b)))
# Check dimensions and give errors if incorrect.
if len(A) != len(b):
raise Exception("Dimensions of A and b are not consistent.")
# 'matrix multiply' and form the string
for i in range(len(b)):
Asum = ""
for j in range(ndim):
Asum += str(A[i][j]) + '*x' + str(j) + ' + '
eqstring += Asum.rstrip(' + ') + ' = ' + str(b[i]) + '\n'
# Inequality constraints
ineqstring = ""
if G != None and h != None:
# If one-dimensional and not in a nested list, add a list layer
try:
ndim = len(G[0])
except:
ndim = len(G)
G = [G]
# Flatten h, in case it's in the form [[0, 1, 2]] for example.
if len(h) == 1:
h = list(ndarray.flatten(asarray(h)))
# Check dimensions and give errors if incorrect.
if len(G) != len(h):
raise Exception("Dimensions of G and h are not consistent.")
# 'matrix multiply' and form the string
for i in range(len(h)):
Gsum = ""
for j in range(ndim):
Gsum += str(G[i][j]) + '*x' + str(j) + ' + '
ineqstring += Gsum.rstrip(' + ') + ' <= ' + str(h[i]) + '\n'
totalconstraints = ineqstring + eqstring
return totalconstraints
async def pull_IMAGE(self, instance, async_lib):
'Periodically update the value'
await instance.write(
value=ndarray.flatten(camera.buffer.get_last_value()))
def qppv(B, msk, nd, wl, nrp, cth, n_itr_th, mx_itr, pfs):
"""This code is adapted from the paper
"Quasi-periodic patterns(QP):Large-scale dynamics in resting state fMRI that correlate"\
with local infraslow electrical activity" Shella Keilholz,D et al.NeuroImage,Volume 84, 1 January 2014."\
The paper implemnts the algorithms for finding QPP in resting state fMRI using matlab"\
This project is an attempt to adopt the algorithm in python, and to integrate into C-PAC.
Input:
------
B: 2D nifti image
msk: mask of the 2D nifti image
nd: number of subjects*number of runs per subject
wl: window length
nrp: number of repetitions
cth: threshold
n_itr_th: number of iterations
mx_itr: maximum number of repetitions
pfs: path to save the template, FTP, ITP and iter files
Returns:
-------
time_course_file: 2D array of time points where QPP is detected in .npy format
ftp_file: 1D array of Final Time Points in .npy format
itp_file: 1D array of Final Time points in .npy format
iter_file: 1D array of iterations in .npy format
Notes:
-----
i) If using a .mat file as an input, save only the image with flag 'v7.0' to make it scipy.io loadmat compatible
(This functionality will soon be replaced by importing with NifTi format only)
ii) To show the peaks found in the signal, add a show=True boolean values in the "find peaks" command.
A "True" value plots the peaks that are found in the signal.
Examples:
--------
>> python detectqppv.py '/path/to/Data/file.mat'
'path/to/mask/file/' 30 6 0.2 0.3 1 15 'path/to/save/results/' 6 1
"""
#get parameters of the image shape to decide the
#shape of the cells,arrays,etc
nT = B.shape[1] #smaller value
nX = B.shape[0] #larger value
#print(nT,nX)
nt = int(nT / nd) #to prevent floating point errors during initializations
nch = nt - wl + 1
nTf = (nX * wl)
#make it a boolean mask - all valies with entries greater than zeros will become 1 the rest will be zero
#no real use of mask anywhere else?
msk = np.zeros((nX, 1))
msk[(np.sum(abs(B)) > 0)] = 1
a = np.where(msk[:, 0] == 1)
B = B[a[0], :]
#defining 3D arrayshere. Each array within the 2D array will finally be a nX*wl shape column vector, which will store the template values
bchf = np.zeros((nT, nX * wl))
bchfn = np.zeros((nT, nX * wl))
#for each subject*run store the template into the bchf array. Instead of using transpose and multiplication, just us dot product of the template square to be stored in bchfn,
#This step. Presumably is done to maximize the peaks that are found within the arrays(eplained below)
for i in range(nd):
for ich in range(nch):
template = B[:, (i) * nt + ich:(i) * nt + wl + ich]
#change template from a row vector to a column vector
template = ndarray.flatten(template)
# insert the template into the bchfn array (this template will be a 1D array)
bchf[i * nt + ich] = template
#normalize
template = template - np.sum(template) / nTf
#get dot product
#template_trans = np.transpose(template)
temp_dot = np.dot(template, template)
template_sqrt = np.sqrt(temp_dot)
template = template / template_sqrt
#add said template into bchfn
bchfn[(i) * nt + ich] = template
#removing nan values and making them 0 to prevent further issues in calculations
A = np.isnan(bchfn)
bchfn[A] = 0
#todo: have to make args.nd and other args.something as just the variable name
#array initialized to later be deleted from the random ITP array
i2x = np.zeros((nd, wl - 1))
#filling the sequence with range of numbers from wl+2 to nt
for i in range(1, nd + 1):
i2x[i - 1, :] = range(i * nt - wl + 2, i * nt + 1)
#delete instances of ITP from i2x
itp = np.arange(1, nT + 1)
i2x = ndarray.flatten(i2x)
itp = np.delete(itp, i2x - 1, 0)
#permute the numbers within ITP
#itp = np.random.permutation(itp)
itp = np.random.permutation(itp)
itp = itp[0:nrp]
#Initialize the time course that will later on be saved
time_course = np.zeros((nrp, nT))
ftp = [[None]] * nrp
iter = np.zeros(nrp)
for irp in range(nrp):
#initialize a matrix c which will hold the templates
c = np.zeros(nT)
for i in range(nd):
for ich in range(nch):
#bchfn_transpose = np.transpose(bchfn[itp[irp]])
bchfn_1 = bchfn[itp[irp]]
bchfn_2 = bchfn[i * nt + ich]
c[(i) * nt + ich] = np.dot(bchfn_1, bchfn_2)
#print(c.shape)
#using MARCUS DEUTRE'S awesome detect_peaks.py function which is a replica of the matlab find peaks function
#switching off show true until it is necessary, in order to test code.
peaks = detect_peaks(c, mph=cth[0], mpd=wl)
#show=True)
#indexes = pu.indexes(c, thresh=c[0])
#You're deleting the first and last instances of the peaks that are now in the 'peaks' array
for i in range(nd):
if i * nt in peaks:
peaks = np.delete(peaks, np.where(peaks == (i) * nt))
if i * nt + nch in peaks:
peaks = np.delete(peaks, np.where(peaks == i * nt + nch))
#house three copies of templates (inefficient) which is then used to decide between the correlation coefficient in the next loop
c_0 = c
c_00 = c
c_000 = c
itr = 1
#peaks_size = peaks.size
#print(peaks)
#print(peaks.shape)
#print(peaks.size)
while itr <= mx_itr:
c = gaussian_filter(c, 0.5)
if itr <= n_itr_th:
ith = 0
else:
ith = 1
th = cth[ith]
tpsgth = peaks
n_tpsgth = np.size(tpsgth)
if n_tpsgth <= 1:
break
template = bchf[tpsgth[0]]
for i in range(1, n_tpsgth):
template = template + bchf[tpsgth[i]]
template = template / n_tpsgth
#perform a repeate of the operations in order to find peaks in the template
#template_trans2=np.transpose(template)
template = template - np.sum(template) / nTf
template = template / np.sqrt(np.dot(template, template))
for i in range(nd):
for ich in range(nch):
c[i * nt + ich] = np.dot(template, bchfn[(i) * nt + ich])
peaks = detect_peaks(c, mph=cth[1], mpd=wl)
for i in range(nd):
if i * nt in peaks:
peaks = np.delete(peaks, np.where(peaks == (i) * nt))
if i * nt + nch in peaks:
peaks = np.delete(peaks, np.where(peaks == i * nt + nch))
c_0_norm = (c_0 - np.mean(c_0)) / (np.std(c_0))
#use the correlation coefficient. It returns a matrix and therefore, the first entry of that matrix will be the correlation coefficient value
if (np.corrcoef(c_0, c)[0, 1] > 0.9999) or (np.corrcoef(
c_00, c)[0, 1] > 0.9999) or (np.corrcoef(c_000, c)[0, 1] >
0.9999):
break
c_000 = c_00
c_00 = c_0
c_0 = c
itr = itr + 1
if n_tpsgth > 1:
time_course[irp, :] = c
ftp[irp] = tpsgth.tolist()
iter[irp] = itr
#save everything!!
plt.plot(template, 'b')
plt.title('Template of QPP(nd=6,wl=30,subjects=7)')
plt.xlabel('avg of func.data of length WL(30)')
plt.show()
mdict = {}
mdict["C"] = time_course
mdict["FTP"] = ftp
mdict["ITER"] = iter
mdict["ITP"] = itp
np.save('time_course_file', time_course)
np.save('ftp_file', ftp)
np.save('iter_file', iter)
np.save('itp_file', itp)
return time_course, ftp, itp, iter
def lossfun_aug():
l_aug = 0
# primary loss: the KL divergence between Y and X
l_Poiss = Y[:, tidx2] * (np.log(Y[:, tidx2]) - np.log(X[:, tidx2]))
l_Poiss = sum(l_Poiss[np.isfinite(l_Poiss)]) + sum(
sum(X[:, tidx2] - Y[:, tidx2]))
l_X = (rho / 2) * sum(
sum((Xhat[:, tidx2] - X[:, tidx2] + np.square(U_X[:, tidx2]) -
np.square(U_X[:, tidx2]))))
l_Fk = 0
[u, s, v] = np.linalg.svd(Fk_nuc)
l_Fk = l_Fk + lambdaFk_nuc * sum(s)
l_Fk = l_Fk + (rho / 2) * sum(
(ndarray.flatten(Fk_nuc) - ndarray.flatten(Fk) + np.square(
ndarray.flatten(U_Fk_nuc)) - np.square(ndarray.flatten(U_Fk_nuc))))
l_Fk = l_Fk + (rho / 2) * sum(
(ndarray.flatten(Fk_nn) - ndarray.flatten(Fk) + np.square(
ndarray.flatten(U_Fk_nn)) - np.square(ndarray.flatten(U_Fk_nn))))
# l_Fk = l_Fk + lambdaFk_TF*sum(Fk_TF*)
# l_Fk = l_Fk + lambdaFk_TF*sum(Fk_TF*)
l_Fu = 0
[u, s, v] = np.linalg.svd(Fu_nuc)
l_Fu = l_Fu + lambdaFu_nuc * sum(s)
l_Fu = l_Fu + (rho / 2) * sum(
(ndarray.flatten(Fu_nuc) - ndarray.flatten(Fu) + np.square(
ndarray.flatten(U_Fu_nuc)) - np.square(ndarray.flatten(U_Fu_nuc))))
l_Fu = l_Fu + (rho / 2) * sum(
(ndarray.flatten(Fu_nn) - ndarray.flatten(Fu) + np.square(
ndarray.flatten(U_Fu_nn)) - np.square(ndarray.flatten(U_Fu_nn))))
l_Sk = 0
l_Sk = l_Sk + (rho / 2) * sum(
(ndarray.flatten(Sk_nn) - ndarray.flatten(Sk) + np.square(
ndarray.flatten(U_Sk_nn)) - np.square(ndarray.flatten(U_Sk_nn))))
l_Su = 0
l_Su = l_Su + (rho / 2) * sum(
(ndarray.flatten(Su_nn) - ndarray.flatten(Su) + np.square(
ndarray.flatten(U_Su_nn)) - np.square(ndarray.flatten(U_Su_nn))))
l_aug = l_Poiss + l_X + l_Fk + l_Fu + l_Sk + l_Su
print 'l_Poiss: ', l_Poiss
print 'l_X: ', l_X
print 'l_Fk: ', l_Fk
print 'l_Fu: ', l_Fu
print 'l_Sk: ', l_Sk
print 'l_Su: ', l_Su
l_aug = l_aug / Y[:, tidx2].size
return l_aug
def X_Theta_to_V(X,Theta):
V = concatenate( ( X.reshape( -1, 1), Theta.reshape( -1, 1) ) )
return ndarray.flatten(asarray( V.T ) )
def score_hand(hand, flip, verbose):
"""Calculates the points in a given hand.
In cribbage, you can get, pairs, fifteens, runs, flushes, and nobs.
Paramerers
hand: 4 card hand (tuple or list)
flip: flipped card (str)
verbose: True/False
Returns: total points (int)
"""
if type(hand) == tuple:
hand = list(hand)
hand = hand + [flip]
nums = [int(c.split('-')[0]) for c in hand]
suits = [c.split('-')[1] for c in hand]
# nobs
jack = 0
if 11 in nums:
flip_suit = flip.split('-')[1]
for card in hand:
if card.split('-') == ['11', flip_suit]:
jack = 1
# pairs
pairs = {i: nums.count(i) for i in nums}
pair_score = sum(
[Cribbage.permu(n, 2) for n in pairs.values() if n > 1])
# flush
if len(unique(suits[:4])) == 1:
if flip.split('-')[1] == suits[0]:
flush_score = 5
else:
flush_score = 4
else:
flush_score = 0
#fifteens and runs
fifteens = list()
runs_raw = list()
for comb in [combinations(hand, i) for i in list(range(6, 1, -1))]:
for c in (list(comb)):
#fifteen
c_adj = [
10 if int(n.split('-')[0]) > 10 else int(n.split('-')[0])
for n in c
] # deals with face cards
if c not in fifteens and sum(c_adj) == 15:
fifteens.append(c)
# runs
nums_a = [int(c_.split('-')[0]) for c_ in c]
l = len(c_adj)
c_sorted = sorted(c)
if l >= 3 and len(unique(nums_a)) == l and (
max(nums_a) - min(nums_a)) == (l - 1):
runs_raw.append(tuple(c_sorted))
runs = [list(x) for x in Cribbage.get_unique_runs(runs_raw)
] # helps in counting points
fifteen_score = len(fifteens) * 2
runs_score = len(ndarray.flatten(asarray(runs)))
if verbose:
pair_explain = [
"{} {}s".format(v, k) for k, v in pairs.items() if v > 1
]
s = """Jack: {}\npairs({}): {}\nfifteens({}): {}\nruns({}): {}\nflush: {}"""
print(
s.format(jack, pair_score, pair_explain, fifteen_score,
fifteens, runs_score, runs, flush_score))
return int(jack + pair_score + flush_score + fifteen_score +
runs_score)
def X_Theta_to_V(X, Theta):
V = concatenate((X.reshape(-1, 1), Theta.reshape(-1, 1)))
return ndarray.flatten(asarray(V.T))
for i in range(1, len(extractor[:, 1, 1])):
meanIntensityList.append(np.mean(extractor[i, :, :]))
plt.plot(range(1, len(extractor[:, 1, 1])),
(meanIntensityList > meanIntensity))
#plt.hlines(np.mean(meanIntensityList)+stdIntensity/10,0,len(extractor[:,1,1]))
plt.show
#ps(anisotropicExtract[:,:,:])
#%%
ws = sitk.MorphologicalWatershed(anisotropicExtract[30:180, :, :],
level=5,
markWatershedLine=False,
fullyConnected=False)
#ws1=(ws==0)
#ps(ws)
c = (np.bincount(ndarray.flatten(s2a(ws))))
#c.argmax()
wsArr = s2a(ws)
wsList = np.nonzero(wsArr == c.argmax())
randomList = []
for i in range(500):
randomList.append(np.random.randint(0, len(wsList[0])))
globList = []
for el in randomList:
seedLoc = [wsList[0][el], wsList[1][el], wsList[2][el]]
globList.append(glob(anisotropicExtract, seedLoc, 30))
c = c + 1
globMeans = []
for el in globList:
globMeans.append(el[1])
seedLocation = globList[globMeans.index(np.min(globMeans))][0]
def linear_symbolic(A=None, b=None, G=None, h=None):
"""Convert linear equality and inequality constraints from matrices to a
symbolic string of the form required by mystic's constraint parser.
Inputs:
A -- (ndarray) matrix of coefficients of linear equality constraints
b -- (ndarray) vector of solutions of linear equality constraints
G -- (ndarray) matrix of coefficients of linear inequality constraints
h -- (ndarray) vector of solutions of linear inequality constraints
NOTE: Must provide A and b; G and h; or A, b, G, and h;
where Ax = b and Gx <= h.
For example:
>>> A = [[3., 4., 5.],
... [1., 6., -9.]]
>>> b = [0., 0.]
>>> G = [1., 0., 0.]
>>> h = [5.]
>>> print linear_symbolic(A,b,G,h)
1.0*x0 + 0.0*x1 + 0.0*x2 <= 5.0
3.0*x0 + 4.0*x1 + 5.0*x2 = 0.0
1.0*x0 + 6.0*x1 + -9.0*x2 = 0.0
"""
eqstring = ""
# Equality constraints
if A is not None and b is not None:
# If one-dimensional and not in a nested list, add a list layer
try:
ndim = len(A[0])
except:
ndim = len(A)
A = [A]
# Flatten b, in case it's in the form [[0, 1, 2]] for example.
if len(b) == 1:
b = ndarray.flatten(asarray(b)).tolist()
# Check dimensions and give errors if incorrect.
if len(A) != len(b):
raise Exception("Dimensions of A and b are not consistent.")
# 'matrix multiply' and form the string
for i in range(len(b)):
Asum = ""
for j in range(ndim):
Asum += str(A[i][j]) + '*x' + str(j) + ' + '
eqstring += Asum.rstrip(' + ') + ' = ' + str(b[i]) + '\n'
# Inequality constraints
ineqstring = ""
if G is not None and h is not None:
# If one-dimensional and not in a nested list, add a list layer
try:
ndim = len(G[0])
except:
ndim = len(G)
G = [G]
# Flatten h, in case it's in the form [[0, 1, 2]] for example.
if len(h) == 1:
h = ndarray.flatten(asarray(h)).tolist()
# Check dimensions and give errors if incorrect.
if len(G) != len(h):
raise Exception("Dimensions of G and h are not consistent.")
# 'matrix multiply' and form the string
for i in range(len(h)):
Gsum = ""
for j in range(ndim):
Gsum += str(G[i][j]) + '*x' + str(j) + ' + '
ineqstring += Gsum.rstrip(' + ') + ' <= ' + str(h[i]) + '\n'
totalconstraints = ineqstring + eqstring
return totalconstraints
