def gen_mixture_data(n, mprop=.1):
""" Generate 2-feature mixture data """
x = np.zeros((n, 2,))
weights = [0.3, 0.7]
mu = [-1.0, 3.0]
for i in range(n):
k = pflip(weights)
m = mu[k]
x[i, :] = np.random.normal(m, size=2)
df = pd.DataFrame(x)
df.columns = ['x_1', 'x_2']
return df
df = gen_mixture_data(200)
engine = Engine(df, n_models=8)
engine.init_models()
engine.run(200)
# find max value in x_1
amin = np.argmin(np.abs(df['x_1']-3.0))
resimp = engine.impute('x_1', [amin])
print(resimp)
""" Generate 2-feature mixture data """
x = np.zeros((
n,
2,
))
weights = [0.3, 0.7]
mu = [-1.0, 3.0]
for i in range(n):
k = pflip(weights)
m = mu[k]
x[i, :] = np.random.normal(m, size=2)
df = pd.DataFrame(x)
df.columns = ['x_1', 'x_2']
return df
df = gen_mixture_data(200)
engine = Engine(df, n_models=8)
engine.init_models()
engine.run(200)
# find max value in x_1
amin = np.argmin(np.abs(df['x_1'] - 3.0))
resimp = engine.impute('x_1', [amin])
print(resimp)
