def onerun(shapefunc, n=250, n_iter=100, n_models=8, subsample_size=None):
xo, yo = shapefunc(n)
s1 = pd.Series(xo)
s2 = pd.Series(yo)
df = pd.concat([s1, s2], axis=1)
df.columns = ['x', 'y']
engine = Engine(df, n_models=n_models, use_mp=True)
engine.init_models(subsample_size=subsample_size)
engine.run(n_iter)
xy = engine.sample(['x', 'y'], n=n)
xe = xy[:, 0]
ye = xy[:, 1]
return xo, yo, xe, ye
def onerun(shapefunc, n=250, n_iter=100, n_models=8, subsample_size=None):
xo, yo = shapefunc(n)
s1 = pd.Series(xo)
s2 = pd.Series(yo)
df = pd.concat([s1, s2], axis=1)
df.columns = ['x', 'y']
engine = Engine(df, n_models=n_models, use_mp=True)
engine.init_models(subsample_size=subsample_size)
engine.run(n_iter)
xy = engine.sample(['x', 'y'], n=n)
xe = xy[:, 0]
ye = xy[:, 1]
return xo, yo, xe, ye
import seaborn as sns
from baxcat.engine import Engine
x = np.hstack((
np.random.randn(100) - 6,
np.random.randn(100)*3,
np.random.randn(100) + 6,))
s1 = pd.Series(x)
df = pd.DataFrame(s1, columns=['x'])
engine = Engine(df, n_models=8)
engine.init_models()
engine.run(100)
y = engine.sample('x', n=300)
plt.subplot(1, 2, 1)
sns.distplot(x, bins=30, label='original')
sns.distplot(y, bins=30, label='model')
plt.xlim([-10, 10])
engine_sub = Engine(df, n_models=8)
engine_sub.init_models(.5)
engine_sub.run(100)
y = engine_sub.sample('x', n=300)
plt.subplot(1, 2, 2)
sns.distplot(x, bins=30, label='original')
sns.distplot(y, bins=30, label='model')
plt.xlim([-10, 10])
fname = func.__name__
s = pd.Series([fname] * n)
df = pd.concat([s, pd.Series(xo), pd.Series(yo)], axis=1)
df.columns = ['func', 'x', 'y']
dfs.append(df)
ax = axes[0, i]
ax.scatter(xo, yo, color='crimson', alpha=.3)
ax = axes[1, i]
ax.scatter(xe, ye, color='gray', alpha=.3)
ax.set_xlim(axes[0, i].get_xlim())
ax.set_ylim(axes[0, i].get_ylim())
df = pd.concat(dfs, ignore_index=True)
engine = Engine(df, n_models=8)
engine.init_models()
engine.run(1000, checkpoint=20)
dfs = []
for i, func in enumerate(funcs):
func_name = func.__name__
x = engine.sample(['x', 'y'], given=[('func', func_name)], n=n)
ax = axes[2, i]
ax.scatter(x[:, 0], x[:, 1], color='navy', alpha=.3)
ax.set_xlim(axes[0, i].get_xlim())
ax.set_ylim(axes[0, i].get_ylim())
plt.show()
from baxcat.engine import Engine
x = np.hstack((
np.random.randn(100) - 6,
np.random.randn(100) * 3,
np.random.randn(100) + 6,
))
s1 = pd.Series(x)
df = pd.DataFrame(s1, columns=['x'])
engine = Engine(df, n_models=8)
engine.init_models()
engine.run(100)
y = engine.sample('x', n=300)
plt.subplot(1, 2, 1)
sns.distplot(x, bins=30, label='original')
sns.distplot(y, bins=30, label='model')
plt.xlim([-10, 10])
engine_sub = Engine(df, n_models=8)
engine_sub.init_models(.5)
engine_sub.run(100)
y = engine_sub.sample('x', n=300)
plt.subplot(1, 2, 2)
sns.distplot(x, bins=30, label='original')
sns.distplot(y, bins=30, label='model')
plt.xlim([-10, 10])
fname = func.__name__
s = pd.Series([fname]*n)
df = pd.concat([s, pd.Series(xo), pd.Series(yo)], axis=1)
df.columns = ['func', 'x', 'y']
dfs.append(df)
ax = axes[0, i]
ax.scatter(xo, yo, color='crimson', alpha=.3)
ax = axes[1, i]
ax.scatter(xe, ye, color='gray', alpha=.3)
ax.set_xlim(axes[0, i].get_xlim())
ax.set_ylim(axes[0, i].get_ylim())
df = pd.concat(dfs, ignore_index=True)
engine = Engine(df, n_models=8)
engine.init_models()
engine.run(1000, checkpoint=20)
dfs = []
for i, func in enumerate(funcs):
func_name = func.__name__
x = engine.sample(['x', 'y'], given=[('func', func_name)], n=n)
ax = axes[2, i]
ax.scatter(x[:, 0], x[:, 1], color='navy', alpha=.3)
ax.set_xlim(axes[0, i].get_xlim())
ax.set_ylim(axes[0, i].get_ylim())
plt.show()