def generate_princomp(xo, filen='testsave.py'):
# import mlabwrap only when run as script
import mlabwrap
from mlabwrap import mlab
np.set_printoptions(precision=14, linewidth=100)
data = HoldIt('data')
data.xo = xo
data.save(filename='testsave.py', comment='generated data, divide by 1000')
res_princomp = HoldIt('princomp1')
res_princomp.coef, res_princomp.factors, res_princomp.values = \
mlab.princomp(x, nout=3)
res_princomp.save(filename=filen, header=False,
comment='mlab.princomp(x, nout=3)')
res_princomp = HoldIt('princomp2')
res_princomp.coef, res_princomp.factors, res_princomp.values = \
mlab.princomp(x[:20,], nout=3)
np.set_printoptions(precision=14, linewidth=100)
res_princomp.save(filename=filen, header=False,
comment='mlab.princomp(x[:20,], nout=3)')
res_princomp = HoldIt('princomp3')
res_princomp.coef, res_princomp.factors, res_princomp.values = \
mlab.princomp(x[:20,]-x[:20,].mean(0), nout=3)
np.set_printoptions(precision=14, linewidth=100)
res_princomp.save(filename=filen, header=False,
comment='mlab.princomp(x[:20,]-x[:20,].mean(0), nout=3)')
def generate_princomp(xo, filen='testsave.py'):
# import mlabwrap only when run as script
from mlabwrap import mlab
np.set_printoptions(precision=14, linewidth=100)
data = HoldIt('data')
data.xo = xo
data.save(filename='testsave.py', comment='generated data, divide by 1000')
res_princomp = HoldIt('princomp1')
res_princomp.coef, res_princomp.factors, res_princomp.values = \
mlab.princomp(x, nout=3)
res_princomp.save(filename=filen,
header=False,
comment='mlab.princomp(x, nout=3)')
res_princomp = HoldIt('princomp2')
res_princomp.coef, res_princomp.factors, res_princomp.values = \
mlab.princomp(x[:20,], nout=3)
np.set_printoptions(precision=14, linewidth=100)
res_princomp.save(filename=filen,
header=False,
comment='mlab.princomp(x[:20,], nout=3)')
res_princomp = HoldIt('princomp3')
res_princomp.coef, res_princomp.factors, res_princomp.values = \
mlab.princomp(x[:20,]-x[:20,].mean(0), nout=3)
np.set_printoptions(precision=14, linewidth=100)
res_princomp.save(filename=filen,
header=False,
comment='mlab.princomp(x[:20,]-x[:20,].mean(0), nout=3)')
def dim_reduce_pca(self, tables, dims):
if not dims:
return [0] * len(tables), [0] * len(tables), None
average_vectors = np.array([t.get_average(*dims) for t in tables])
print average_vectors.shape
from mlabwrap import mlab
#extra_points, mapping = mlab.compute_mapping(
# average_vectors, 'PCA', 2, nout=2)
coeffs, extra_points = mlab.princomp(average_vectors, nout=2)
print extra_points.shape
return extra_points.T[0], extra_points.T[1], coeffs
def dim_reduce_pca(self, tables, dims):
if not dims:
return [0] * len(tables), [0] * len(tables), None
average_vectors = np.array([t.get_average(*dims) for t in tables])
print average_vectors.shape
from mlabwrap import mlab
#extra_points, mapping = mlab.compute_mapping(
# average_vectors, 'PCA', 2, nout=2)
coeffs, extra_points = mlab.princomp(average_vectors, nout=2)
print extra_points.shape
return extra_points.T[0], extra_points.T[1], coeffs
