def test_LM_model(ndims, data_t, data_v):
"""
Example that demonstrates how to call the model.
"""
# get hyperparameters for model
hyperp = get_hyperp()
# generate 50 training data and 20 validation data locations of dim=1
ndata = data_t # Training Data
ndata_v = data_v # Validation Data
pdata = ndims # K
X = generate_X(ndata, pdata)
X_v = generate_X(ndata_v, pdata)
# intialie true model randomly and draw observations from it
true_model = EM_algo_LM(hyperp, ndata=ndata, pdata=pdata)
Y, Z = generate_YZ(
X, true_model
) # TODO : change distribution.draw to draw samples from the mixture model
Y_v, Z_v = generate_YZ(X_v, true_model)
print("Generated %d training data and %d validation data from true model:" % \
(ndata, ndata_v))
true_model.print_p()
print("")
# generate a model for estimating the parameters of the
# true model based on the observations (X, Y) we just made
model = EM_algo_LM(hyperp, X, Y)
i, logl, r = model.EM_fit()
print("Model fit (logl %.2f) after %d iterations (%s reached)" % \
(logl, i, r))
print("")
print("MAP estimate of true model parameters:")
model.print_p()
print("")
# crossvalidate the estimated model with the validation data
fit_params = model.get_p()
model_v = EM_algo_LM(hyperp, X_v, Y_v)
model_v.set_p(fit_params)
logl, ll = model_v.logl()
print("Crossvalidated logl: %.2f" % (logl))
# if possible, plot samples, true model and estimated model
if pdata != 1:
return
plt.scatter(X, Y, s=20, c='black', label="Training data")
plt.scatter(X_v, Y_v, s=20, c='orange', label="Validation data")
x = arange(min(X) - 0.1, max(X) + 0.1, 0.1)
print_linear_model(x, true_model.get_p()["phi"], \
true_model.get_p()["sigma2"], 'red', "True model")
print_linear_model(x, model.get_p()["phi"], \
model.get_p()["sigma2"], 'blue', "Predicted model")
plt.legend(loc=1)
plt.xlim(min(x), max(x))
plt.xlabel("x")
plt.ylabel("y")
plt.show()
from em_algo_mm import EM_algo_MM
from em_algo_lm import EM_algo_LM
from generator import get_hyperp
from numpy import arange, min, max, sqrt, mean, std, hstack, vstack, shape, load, empty, save
from numpy.random import shuffle
import pandas as pd
NUM_EXP = 10
# get hyperparameters for model
hyperp = get_hyperp()
# load generated GMM model Data
path = "/Users/lixinyue/Documents/Machine_Learning_Advanced_Probabilistic_Methods/example_code_python_change/"
X = load(path + 'X.npy')
Y = load(path + 'Y.npy')
Z = load(path + 'Z.npy')
X_v = load(path + 'X_v.npy')
Y_v = load(path + 'Y_v.npy')
Z_v = load(path + 'Z_v.npy')
# generate a model for estimating the parameters of the
# true model based on the observations (X, Y) we just made
def GMM_fit(X,Y,X_v,Y_v):
model = EM_algo_MM(hyperp, X, Y)
i, logl_train, r = model.EM_fit()
print("Model fit (logl %.2f) after %d iterations (%s reached)" % \
(logl_train, i, r))
print("")
print("MAP estimate of true model parameters:")
model.print_map()
from em_algo_mm import EM_algo_MM
from em_algo_lm import EM_algo_LM
from generator import get_hyperp
from numpy import arange, min, max, sqrt, mean, std, hstack, vstack, shape, load, empty, save
from numpy.random import shuffle
import pandas as pd
NUM_EXP = 10
# get hyperparameters for model
hyperp = get_hyperp()
# load generated GMM model Data
path = "/Users/lixinyue/Documents/Machine_Learning_Advanced_Probabilistic_Methods/example_code_python_change/"
X = load(path + 'X.npy')
Y = load(path + 'Y.npy')
Z = load(path + 'Z.npy')
X_v = load(path + 'X_v.npy')
Y_v = load(path + 'Y_v.npy')
Z_v = load(path + 'Z_v.npy')
# generate a model for estimating the parameters of the
# true model based on the observations (X, Y) we just made
def GMM_fit(X, Y, X_v, Y_v):
model = EM_algo_MM(hyperp, X, Y)
i, logl_train, r = model.EM_fit()
print("Model fit (logl %.2f) after %d iterations (%s reached)" % \
(logl_train, i, r))
print("")
print("MAP estimate of true model parameters:")
def test_MM_model(ndims, data_t, data_v, iters):
"""
Example that demonstrates how to call the model.
"""
# get hyperparameters for model
hyperp = get_hyperp()
# generate 50 training data and 20 validation data locations of dim=1
ndata = data_t # Training Data
ndata_v = data_v # Validation Data
pdata = ndims # K
X = generate_X(ndata, pdata)
X_v = generate_X(ndata_v, pdata)
# intialize true model randomly and draw observations from it
true_model = EM_algo_MM(hyperp, ndata=ndata, pdata=pdata)
# true_model.print_p()
Y, Z = generate_YZ(
X, true_model
) # TODO : change distribution.draw to draw samples from the mixture model
Y_v, Z_v = generate_YZ(X_v, true_model)
print("Generated %d training data and %d validation data from true model:" % \
(ndata, ndata_v))
true_model.print_p()
print("")
lowest_mse = 999
params = None
for i in xrange(iters):
# generate a model for estimating the parameters of the
# true model based on the observations (X, Y) we just made
model = EM_algo_MM(hyperp, X, Y)
# model.print_p()
i, logl, r = model.EM_fit()
# plt.plot(model.get_loglVals())
# plt.show()
# plt.plot(vals)
# plt.show()
print("Model fit (logl %.2f) after %d iterations (%s reached)" % \
(logl, i, r))
print("")
print("MAP estimate of true model parameters:")
model.print_p()
print("")
# crossvalidate the estimated model with the validation data
fit_params = model.get_p()
model_v = EM_algo_MM(hyperp, X_v, Y_v)
model_v.set_p(fit_params)
logl, ll = model_v.logl()
print("Crossvalidated logl: %.2f" % (logl))
# print("DEBUG MSE")
# print zip((Z_v*(X_v.dot(fit_params["phi_1"]))),((1-Z_v)*(X_v.dot(fit_params["phi_2"]))))
error = mse((Z_v * X_v.dot(model_v.get_p()["phi_1"])) +
((1 - Z_v) * X_v.dot(model_v.get_p()["phi_2"])), Y_v)
if error < lowest_mse:
lowest_mse = error
params = fit_params
print("LOWEST MSE = %.3f" % lowest_mse)
print("lowest MSE model Params :")
testmodel = EM_algo_MM(hyperp, ndata=ndata, pdata=pdata)
testmodel.set_p(params)
testmodel.print_p()
testmodel = None
print("TRUE Model MSE :")
mse((Z_v * X_v.dot(true_model.get_p()["phi_1"])) +
((1 - Z_v) * X_v.dot(true_model.get_p()["phi_2"])), Y_v)
# if possible, plot samples, true model and estimated model
if pdata != 1:
return
plt.scatter(X, Y, s=20, c='black', label="Training data")
# plt.scatter(X_v, Y_v, s=20, c='orange', label="Validation data")
x = arange(min(X) - 0.1, max(X) + 0.1, 0.1)
print_mixture_model(x, true_model.get_p()["phi_1"], true_model.get_p()["phi_2"], \
true_model.get_p()["sigma2_1"], true_model.get_p()["sigma2_2"], 'red', "True model")
print_mixture_model(x, model.get_p()["phi_1"], model.get_p()["phi_2"], \
model.get_p()["sigma2_1"], model.get_p()["sigma2_2"], 'blue', "Predicted model")
plt.legend(loc=1)
plt.xlim(min(x), max(x))
plt.xlabel("x")
plt.ylabel("y")
plt.show()
print "end"
def main():
"""
Executed when program is run.
"""
ndims = int(sys.argv[1])
data_t = int(sys.argv[2])
data_v = 50
# -------- Generating Data ---------
# get hyperparameters for model
hyperp = get_hyperp()
# generate 50 training data and 20 validation data locations of dim=1
ndata = data_t # Training Data
ndata_v = data_v # Validation Data
pdata = ndims # K
X = generate_X(ndata, pdata)
X_v = generate_X(ndata_v, pdata)
# intialie true model randomly and draw observations from it
true_model = EM_algo_MM(hyperp, ndata=ndata, pdata=pdata)
phi1 = true_model.get_p()["phi_1"]
phi2 = true_model.get_p()["phi_2"]
print(phi1)
print(phi2)
print("\x1b[31;m COSINE SIMILARITY = %.3f \x1b[0m" %
(1 - cosine(phi1, phi2)))
Y, Z = generate_YZ(
X, true_model
) # TODO : change distribution.draw to draw samples from the mixture model
Y_v, Z_v = generate_YZ(X_v, true_model)
print("Generated %d training data and %d validation data from true model: %s" % \
(ndata, ndata_v, true_model.get_model_type()))
true_model.print_p()
print("")
# ----------------------------------
print("Mixture Model")
print("")
test_MM_model(ndims=ndims,
data_t=data_t,
data_v=data_v,
hyperp=hyperp,
X=X,
Y=Y,
Z=Z,
X_v=X_v,
Y_v=Y_v,
Z_v=Z_v,
true_model=true_model,
iters=10)
print("")
print("Starting program")
print("")
test_LM_model(ndims=ndims,
data_t=data_t,
data_v=data_v,
hyperp=hyperp,
X=X,
Y=Y,
Z=Z,
X_v=X_v,
Y_v=Y_v,
Z_v=Z_v,
true_model=true_model,
iters=10)
print("")
