def princomp_B(A,numpc=0):
"""
Compute 1st Principal Component.
Function modified from: http://glowingpython.blogspot.ch/2011/07/principal-component-analysis-with-numpy.html
Parameters
----------
A : object of type MstConfiguration
Contains the following attributes: subtract_column_mean_at_start (bool), debug (bool), use_gfp_peaks (bool), force_avgref (bool), set_gfp_all_1 (bool), use_smoothing (bool), gfp_type_smoothing (string),
smoothing_window (int), use_fancy_peaks (bool), method_GFPpeak (string), original_nr_of_maps (int), seed_number (int), max_number_of_iterations (int), ERP (bool), correspondance_cutoff (double):
numpc : int
number of principal components
Returns
-------
coeff: array
first principal component
"""
M=A.T
a=numpy.dot(M,M.T) #get covariance matrix by matrix multiplication of data with transposed data
[latent,coeff]=eig(a)
p = size(coeff,axis=1)
idx = argsort(latent) # sorting the eigenvalues
idx = idx[::-1] # in ascending order
# sorting eigenvectors according to the sorted eigenvalues
coeff = coeff[:,idx]
#latent = latent[idx] # sorting eigenvalues
if numpc < p or numpc >= 0:
coeff = coeff[:,range(numpc)] # cutting some PCs
#score = dot(coeff.T,M) # projection of the data in the new space
return coeff
def princomp_B(A, numpc=0):
"""
Compute 1st Principal Component.
Function modified from: http://glowingpython.blogspot.ch/2011/07/principal-component-analysis-with-numpy.html
Parameters
----------
A : object of type MstConfiguration
Contains the following attributes: subtract_column_mean_at_start (bool), debug (bool), use_gfp_peaks (bool), force_avgref (bool), set_gfp_all_1 (bool), use_smoothing (bool), gfp_type_smoothing (string),
smoothing_window (int), use_fancy_peaks (bool), method_GFPpeak (string), original_nr_of_maps (int), seed_number (int), max_number_of_iterations (int), ERP (bool), correspondance_cutoff (double):
numpc : int
number of principal components
Returns
-------
coeff: array
first principal component
"""
M = A.T
a = np.dot(
M, M.T
) #get covariance matrix by matrix multiplication of data with transposed data
[latent, coeff] = eig(a)
p = size(coeff, axis=1)
idx = argsort(latent) # sorting the eigenvalues
idx = idx[::-1] # in ascending order
# sorting eigenvectors according to the sorted eigenvalues
coeff = coeff[:, idx]
#latent = latent[idx] # sorting eigenvalues
if numpc < p or numpc >= 0:
coeff = coeff[:, list(range(numpc))] # cutting some PCs
#score = dot(coeff.T,M) # projection of the data in the new space
return coeff
def get_prediction(self, a,b,c, ok_index, restrict_vocab=30000):
ok_vocab = dict(sorted(iteritems(self.model.vocab),
key=lambda item: -item[1].count)
[:restrict_vocab])
ok_index = set(v.index for v in itervalues(ok_vocab))
ignore = set(self.model.vocab[v].index for v in [a, b, c]) # indexes of words to ignore
positive = [b, c]
negative = [a]
for index in argsort(self.model.most_similar(self.model,
positive,
negative,
False))[::-1]:
if index in ok_index and index not in ignore:
predicted = self.model.index2word[index]
break
return predicted
elementSizeAll.append(elementSize)
numElementsAll.append(numElements)
print(np.array(numElementsAll))
print(np.array(elementSizeAll))
heaveforceAll = np.array(heaveforceAll)
pitchmomentAll = np.array(pitchmomentAll)
elementSizeAll = np.array(elementSizeAll)
metaCenter = zB + Sxx / V0
GML = metaCenter - zG
sinkage = heaveforceAll / (g * density * A)
pitch = pitchmomentAll / ((g * density * V0 * GML))
perm = argsort(Frh)
sink_unc = 0 * sinkage
for i in range(sinkage.shape[0]):
sink_unc[i, :] = NumericalUncertainty(elementSizeAll[i, :], sinkage[i, :],
showNumericalUncertainty)
cnt = sinkage.shape[1] - 1
if (showHeaveForce):
plt.figure(1)
plt.plot(Frh[perm], heaveforceAll[perm, cnt], 'o-', label="BEM")
plt.xlabel(r"$Fr_h$")
plt.ylabel(r"$F_z [N]$")
plt.grid(True)
plt.legend()
