mirror of
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99 lines
2.7 KiB
Python
99 lines
2.7 KiB
Python
#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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"""Provide some examples using GPR.
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"""
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import sys # to check if Python 3 or 2
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import numpy as np
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import matplotlib.pyplot as plt
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from pyrobolearn.models.gp import GPR
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from pyrobolearn.utils.converter import torch_to_numpy
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# create data: Generate random sample following a sine curve
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# Ref: https://scikit-learn.org/stable/auto_examples/mixture/plot_gmm_sin.html#sphx-glr-auto-examples-mixture-\
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# plot-gmm-sin-py
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n_samples = 100
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np.random.seed(0)
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X = np.zeros((n_samples, 2))
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step = 4. * np.pi / n_samples
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for i in range(X.shape[0]):
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x = i * step - 6.
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X[i, 0] = x + np.random.normal(0, 0.1)
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X[i, 1] = 3. * (np.sin(x) + np.random.normal(0, .2))
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x, y = X[:, 0], X[:, 1]
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xlim, ylim = [-8, 8], [-8, 8]
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# plot data
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plt.title('Training data')
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plt.scatter(x, y)
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plt.xlim(xlim)
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plt.ylim(ylim)
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plt.show()
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# create GPR
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model = GPR()
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# plot prior possible functions
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f = model.sample(x, num_samples=10, to_numpy=True)
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plt.title('Sampled functions from prior distribution')
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plt.scatter(x, y, alpha=0.3)
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plt.plot(x, f.T)
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plt.xlim(xlim)
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plt.ylim(ylim)
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plt.show()
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# compute log likelihoods
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print("\nBefore training:")
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print("Log likelihood: {}".format(model.log_likelihood(x, y, to_numpy=True)))
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print("Log marginal likelihood: {}".format(model.log_marginal_likelihood(x, y, to_numpy=True)))
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# fit the data
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optimizer = 'adam' # 'lbfgs'
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model.fit(x, y, num_iters=100, optimizer=optimizer, verbose=True)
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# compute log likelihoods
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print("\nAfter training:")
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print("Log likelihood: {}".format(model.log_likelihood(x, y, to_numpy=True)))
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print("Log marginal likelihood: {}".format(model.log_marginal_likelihood(x, y, to_numpy=True)))
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# sample function and plot it
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f = model.sample(x, num_samples=1, to_numpy=True)
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plt.title('one sampled function after training')
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plt.scatter(x, y)
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plt.plot(x, f.T, 'k', linewidth=2.)
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plt.xlim(xlim)
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plt.ylim(ylim)
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plt.show()
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# predict prob
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x_test = np.linspace(-10, 10, 101)
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xlim, ylim = [-10, 10], [-10, 10]
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mean_y, var_y = model.predict_prob(x_test, to_numpy=True)
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std_y = np.sqrt(var_y)
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plt.title('Prediction')
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plt.scatter(x, y)
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plt.plot(x_test, mean_y, 'b')
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plt.fill_between(x_test, mean_y-2*std_y, mean_y+2*std_y, facecolor='green', alpha=0.3)
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plt.xlim(xlim)
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plt.ylim(ylim)
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plt.show()
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# Another way to predict
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pred = model.forward(x_test)
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lower, upper = pred.confidence_region()
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plt.title("Another way to predict (see code)")
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plt.plot(x, y, 'k*')
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if sys.version_info[0] < 3: # Python 2
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plt.plot(x_test, torch_to_numpy(pred.mean()), 'b')
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else: # Python 3
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plt.plot(x_test, torch_to_numpy(pred.mean), 'b')
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plt.fill_between(x_test, torch_to_numpy(lower), torch_to_numpy(upper), alpha=0.5)
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plt.xlim(xlim)
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plt.ylim(ylim)
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plt.show()
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