def plot_component_variance(x, y): prim_basis = PrimitiveBasis(n_states=3, domain=[0, 2]) model = MKSHomogenizationModel(basis=prim_basis) model.n_components = 20 model.fit(x, y, periodic_axes=[0, 1]) # Draw the plot containing the PCA variance accumulation draw_component_variance(model.dimension_reducer.explained_variance_ratio_)
def plot_components(x, y, n_comps, linker_model, verbose=2):
prim_basis = PrimitiveBasis(n_states=3, domain=[0, 2])
model = MKSHomogenizationModel(basis=prim_basis,
property_linker=linker_model)
model.n_components = 5
model.fit(x,y,periodic_axes=[0,1])
print model.property_linker.coef_
draw_components([model.reduced_fit_data[0:3, :2],
model.reduced_fit_data[3:6, :2],
model.reduced_fit_data[6:9, :2],
model.reduced_fit_data[9:11, :2],
model.reduced_fit_data[11:14, :2],
model.reduced_fit_data[14:16, :2],
model.reduced_fit_data[16:17, :2],
model.reduced_fit_data[17:18, :2]],
['Ag:0.237 Cu:0.141 v:0.0525',
'Ag:0.237 Cu:0.141 v:0.0593',
'Ag:0.237 Cu:0.141 v:0.0773',
'Ag:0.237 Cu:0.141 v:0.0844',
'Ag:0.239 Cu:0.138 v:0.0791',
'Ag:0.239 Cu:0.138 v:0.0525',
'Ag:0.237 Cu:0.141 v:0.0914',
'Ag:0.237 Cu:0.141 v:0.0512'])
def test_n_components_change():
from pymks import MKSHomogenizationModel, DiscreteIndicatorBasis
dbasis = DiscreteIndicatorBasis(n_states=2)
model = MKSHomogenizationModel(basis=dbasis)
model.n_components = 27
assert model.n_components == 27
def test_n_components_change():
from pymks import MKSHomogenizationModel, DiscreteIndicatorBasis
dbasis = DiscreteIndicatorBasis(n_states=2)
model = MKSHomogenizationModel(basis=dbasis)
model.n_components = 27
assert model.n_components == 27
# 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
