def get_correlations_for_slice(al_data_slice): # prim basis tellis it to use 0,1,2 as the 3 Stats prim_basis = PrimitiveBasis(n_states=3, domain=[0,2]) disc_basis = prim_basis.discretize(al_data_slice) # get the correlations correlations = correlate(disc_basis, periodic_axes=(0, 1)) return correlations
def compute_correlations(x, correlations=None, compute_flat=True):
print "-->Constructing Correlations"
prim_basis = PrimitiveBasis(n_states=3, domain=[0,2])
x_ = prim_basis.discretize(x)
if correlations == None:
x_corr = correlate(x_, periodic_axes=[0, 1])
else:
x_corr = correlate(x_, periodic_axes=[0, 1], correlations=correlations)
if compute_flat:
x_corr_flat = np.ndarray(shape=(x.shape[0], x_corr.shape[1]*x_corr.shape[2]*x_corr.shape[3]))
row_ctr = 0
for row in x_corr:
x_corr_flat[row_ctr] = row.flatten()
row_ctr += 1
return x_corr, x_corr_flat
return x_corr
print "--->RandomForest"
linker = RandomForestClassifier()
params = {'n_estimators':range(1,100,10)}
opt_model = run_gridcv_linkage(y,x_pca,linker,params)
print('---->n_est:'), (opt_model.best_estimator_.n_estimators)
r2_mean, r2_std, mse_mean, mse_std = run_conventional_linkage(y,x_pca,5,opt_model)
quit()
print "-->Constructing Correlations"
prim_basis = PrimitiveBasis(n_states=3, domain=[0,2])
x_ = prim_basis.discretize(x)
x_corr = correlate(x_, periodic_axes=[0, 1])
x_corr_flat = np.ndarray(shape=(samples, x_corr.shape[1]*x_corr.shape[2]*x_corr.shape[3]))
row_ctr = 0
for row in x_corr:
x_corr_flat[row_ctr] = row.flatten()
print x.shape
flat_len = (x.shape[0],) + (np.prod(x.shape[1:]),)
X_train, X_test, y_train, y_test = train_test_split(x.reshape(flat_len), y,
test_size=0.2, random_state=3)
print(x_corr.shape)
print(X_test.shape)
# uncomment to view one containers
#draw_correlations(x_corr[0].real)
def _property2distribution(self, y):
n_bins = int((self.domain[1] - self.domain[0] + self.dx) / self.dx)
p_basis = PrimitiveBasis(n_states=n_bins, domain=self.domain)
return p_basis.discretize(y)[:, 0, :]
def analyze_data_slice(al_data_slice): # prim basis prim_basis = PrimitiveBasis(n_states=3, domain=[0,2]) disc_basis = prim_basis.discretize(al_data_slice) correlations = correlate(disc_basis) return correlations
def analyze_data_slice(al_data_slice):
# prim basis
prim_basis = PrimitiveBasis(n_states=3, domain=[0, 2])
disc_basis = prim_basis.discretize(al_data_slice)
correlations = correlate(disc_basis)
return correlations
from pymks import PrimitiveBasis
from pymks.datasets import make_checkerboard_microstructure
from pymks.stats import autocorrelate
from pymks.stats import crosscorrelate
from pymks.tools import draw_microstructures
from pymks.tools import draw_autocorrelations
from pymks.tools import draw_crosscorrelations
# Make checkerboard microstructure and observe it.
X = make_checkerboard_microstructure(square_size=10, n_squares=6)
draw_microstructures(X)
# Define the basis for 2-point statistics
prim_basis = PrimitiveBasis(n_states=2)
X_ = prim_basis.discretize(X)
# Computing auto-correlatiions of the microstructure function and drawing the same
X_auto = autocorrelate(X,
basis=PrimitiveBasis(n_states=2),
periodic_axes=(0, 1))
correlations = [('white', 'white'), ('black', 'black')]
draw_autocorrelations(X_auto[0], autocorrelations=correlations)
# Checking the volume fraction of both the phases i.e. (0,0) value of auto-correlations
centre = (X_auto.shape[1] + 1) / 2
print('Volume fraction of black phase', X_auto[0, centre, centre, 0])
print('Volume fraction of black phase', X_auto[0, centre, centre, 1])
# Computing the cross correlation of the microstructure function and drawing the same
X_cross = crosscorrelate(X,
# Get a representative slice from the block (or ave or whatever we decide on)
best_slice = get_best_slice(metadatum['data'])
# Get 2-pt Stats for the best slice
print "--->Getting 2pt stats"
metadatum['stats'] = get_correlations_for_slice(best_slice)
print metadata[0]['stats'].shape
# Construct X and Y for PCA and linkage
print "-->Creating X and Y"
i = 0
for metadatum in metadata:
x[i,0:6*metadatum['x']**2] = metadatum['stats'].flatten()
prim_basis = PrimitiveBasis(n_states=3, domain=[0,2])
x_ = prim_basis.discretize(metadata[0]['data'])
x_corr = correlate(x_)
draw_correlations(x_corr.real)
quit()
# Reduce all 2-pt Stats via PCA
# Try linear reg on inputs and outputs
reducer = PCA(n_components=3)
linker = LinearRegression()
model = MKSHomogenizationModel(dimension_reducer=reducer,
property_linker=linker,
compute_correlations=False)
model.n_components = 40
model.fit(metadatum['stats'], y, periodic_axes=[0, 1])
print model.reduced_fit_data