def main(root, pattern='68/*block03/*trial00', frames=10, start=27.5, stop=38.5):
_, jac = jacobian(root, pattern, frames)
n = int(np.sqrt(len(jac.columns) / 9))
pca = lmj.pca.PCA(filename=PCA_FILE)
for v in (0.5, 0.8, 0.9, 0.95, 0.98, 0.99, 0.995, 0.998, 0.999):
print('variance', v, 'components', pca.num_components(v))
if os.path.exists(MODEL):
dl = pickle.load(open(MODEL, 'rb'))
else:
dl = sklearn.decomposition.MiniBatchSparsePCA(
n_components=100, alpha=0.0001, verbose=10)
dl.fit(pca.encode(jac.dropna().values, retain=0.999))
pickle.dump(dl, open(MODEL, 'wb'), -1)
df = pd.DataFrame(pca.decode(dl.components_, retain=0.999), columns=jac.columns)
enc = sklearn.decomposition.sparse_encode(
pca.encode(jac.dropna().loc[start:stop, :], retain=0.999),
dl.components_,
algorithm='omp',
alpha=0.0001,
)
kw = dict(vmin=-10, vmax=10, cmap='RdBu')
lmj.plot.create_axes(111, spines=False).imshow(enc.T, **kw)
lmj.plot.show()
enc = sklearn.decomposition.sparse_encode(
jac.dropna().loc[start:stop, :],
pca.vecs.T[:len(dl.components_)],
algorithm='omp',
alpha=0.0001,
)
kw = dict(vmin=-10, vmax=10, cmap='RdBu')
lmj.plot.create_axes(111, spines=False).imshow(enc.T, **kw)
lmj.plot.show()
def find(i, g, b):
cs = [c for c in jac.columns if '{}/'.format(g) in c and c.endswith(b)]
return df[cs].loc[i, :].values.reshape((n, n))
def plot(where, i, g, b):
ax = lmj.plot.create_axes(where, spines=False)
ax.imshow(find(i, g, b), cmap='RdBu')
for i in range(10):
plot(331, i, 'x', 'x')
plot(332, i, 'x', 'y')
plot(333, i, 'x', 'z')
plot(334, i, 'y', 'x')
plot(335, i, 'y', 'y')
plot(336, i, 'y', 'z')
plot(337, i, 'z', 'x')
plot(338, i, 'z', 'y')
plot(339, i, 'z', 'z')
lmj.plot.gcf().subplots_adjust(left=0, right=1, bottom=0, top=1, wspace=0, hspace=0)
lmj.plot.show()
def main(root, pca):
pca = lmj.pca.PCA(filename=pca)
K = 80
with lmj.plot.axes(spines=True) as ax:
ax.bar(range(K), (pca.vals / pca.vals.sum())[:K], color='#111111', lw=0)
ax.set_xlim(0, K)
ax.set_yticks([0.1, 0.3, 0.5, 0.7])
ax.grid(True)
ax.set_xlabel('Principal Component')
ax.set_ylabel('Fraction of Variance')
for c in (0.9,):
k = pca.num_components(c)
ax.axvline(k, 0, pca.vals[0], color='#d62728', lw=1)
ax.annotate('{:.0f}%'.format(100 * c), xy=(k, 0.4), xytext=(8, 0.3), color='#d62728',
arrowprops=dict(color='#d62728', arrowstyle='->', connectionstyle='angle,angleA=-100,angleB=10,rad=10'))
for c in (0.99,):
k = pca.num_components(c)
ax.axvline(k, 0, pca.vals[0], color='#d62728', lw=1)
ax.annotate('{:.0f}%'.format(100 * c), xy=(k, 0.6), xytext=(8, 0.7), color='#d62728',
arrowprops=dict(color='#d62728', arrowstyle='->', connectionstyle='angle,angleA=-100,angleB=10,rad=10'))
def main(root, pca):
pca = lmj.pca.PCA(filename=pca)
K = 80
with lmj.plot.axes(spines=True) as ax:
ax.bar(range(K), (pca.vals / pca.vals.sum())[:K],
color='#111111',
lw=0)
ax.set_xlim(0, K)
ax.set_yticks([0.1, 0.3, 0.5, 0.7])
ax.grid(True)
ax.set_xlabel('Principal Component')
ax.set_ylabel('Fraction of Variance')
for c in (0.9, ):
k = pca.num_components(c)
ax.axvline(k, 0, pca.vals[0], color='#d62728', lw=1)
ax.annotate(
'{:.0f}%'.format(100 * c),
xy=(k, 0.4),
xytext=(8, 0.3),
color='#d62728',
arrowprops=dict(
color='#d62728',
arrowstyle='->',
connectionstyle='angle,angleA=-100,angleB=10,rad=10'))
for c in (0.99, ):
k = pca.num_components(c)
ax.axvline(k, 0, pca.vals[0], color='#d62728', lw=1)
ax.annotate(
'{:.0f}%'.format(100 * c),
xy=(k, 0.6),
xytext=(8, 0.7),
color='#d62728',
arrowprops=dict(
color='#d62728',
arrowstyle='->',
connectionstyle='angle,angleA=-100,angleB=10,rad=10'))
def main(root,
pattern='68/*block03/*trial00',
frames=10,
start=27.5,
stop=38.5):
_, jac = jacobian(root, pattern, frames)
n = int(np.sqrt(len(jac.columns) / 9))
pca = lmj.pca.PCA(filename=PCA_FILE)
for v in (0.5, 0.8, 0.9, 0.95, 0.98, 0.99, 0.995, 0.998, 0.999):
print('variance', v, 'components', pca.num_components(v))
if os.path.exists(MODEL):
dl = pickle.load(open(MODEL, 'rb'))
else:
dl = sklearn.decomposition.MiniBatchSparsePCA(n_components=100,
alpha=0.0001,
verbose=10)
dl.fit(pca.encode(jac.dropna().values, retain=0.999))
pickle.dump(dl, open(MODEL, 'wb'), -1)
df = pd.DataFrame(pca.decode(dl.components_, retain=0.999),
columns=jac.columns)
enc = sklearn.decomposition.sparse_encode(
pca.encode(jac.dropna().loc[start:stop, :], retain=0.999),
dl.components_,
algorithm='omp',
alpha=0.0001,
)
kw = dict(vmin=-10, vmax=10, cmap='RdBu')
lmj.plot.create_axes(111, spines=False).imshow(enc.T, **kw)
lmj.plot.show()
enc = sklearn.decomposition.sparse_encode(
jac.dropna().loc[start:stop, :],
pca.vecs.T[:len(dl.components_)],
algorithm='omp',
alpha=0.0001,
)
kw = dict(vmin=-10, vmax=10, cmap='RdBu')
lmj.plot.create_axes(111, spines=False).imshow(enc.T, **kw)
lmj.plot.show()
def find(i, g, b):
cs = [c for c in jac.columns if '{}/'.format(g) in c and c.endswith(b)]
return df[cs].loc[i, :].values.reshape((n, n))
def plot(where, i, g, b):
ax = lmj.plot.create_axes(where, spines=False)
ax.imshow(find(i, g, b), cmap='RdBu')
for i in range(10):
plot(331, i, 'x', 'x')
plot(332, i, 'x', 'y')
plot(333, i, 'x', 'z')
plot(334, i, 'y', 'x')
plot(335, i, 'y', 'y')
plot(336, i, 'y', 'z')
plot(337, i, 'z', 'x')
plot(338, i, 'z', 'y')
plot(339, i, 'z', 'z')
lmj.plot.gcf().subplots_adjust(left=0,
right=1,
bottom=0,
top=1,
wspace=0,
hspace=0)
lmj.plot.show()
