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83 lines
2.4 KiB
Python
83 lines
2.4 KiB
Python
#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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"""Provide some examples using KMP.
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See also the `kmp.py` example beforehand. In this example, we delve a bit deeper into KMP using 2D letters as training
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data.
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"""
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import numpy as np
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from scipy.io import loadmat
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import matplotlib.pyplot as plt
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from pyrobolearn.models.gmm import plot_gmr, plot_gmm
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from pyrobolearn.models.kmp import KMP, RBF
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# KMP parameters (play with them)
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mean_reg = 1. # 0.01, 0.1, 1.
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covariance_reg = 100. # 0.1
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lengthscale = 1./6
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# load the training data
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G = loadmat('../../data/2Dletters/G.mat') # dict
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demos = G['demos'] # shape (1,N)
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n_demos = demos.shape[1]
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dim = demos[0, 0][0, 0][0].shape[0]
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length = demos[0, 0][0, 0][0].shape[1]
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# plot the training data (x,y)
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X = []
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xlim, ylim = [-10, 10], [-10, 10]
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plt.title("Training Data")
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plt.xlim(xlim)
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plt.ylim(ylim)
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for i in range(0, n_demos, 2):
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demo = demos[0, i][0, 0][0] # shape (2, 200)
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plt.plot(demo[0], demo[1])
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X.append(demo.T)
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plt.show()
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# reshape training data (add time in addition to (x,y), thus we now have (t,x,y))
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time_linspace = np.linspace(0, 2., length) # shape (200,)
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times = np.asarray([time_linspace for _ in range(len(X))]) # shape (N,200)
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X = np.asarray(X) # shape (N, 200, 2)
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X = np.dstack((times, X)) # shape (N, 200, 3)
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print(X.shape)
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# create KMP
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print("Creating the KMP model")
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kernel = RBF(lengthscale=lengthscale)
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kmp = KMP(kernel_fct=kernel)
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# fit a KMP on the data
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print("Training the KMP...")
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kmp.fit(X=X[:, :, [0]], Y=X[:, :, 1:], gmm_num_components=7, mean_reg=mean_reg, covariance_reg=covariance_reg,
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gmm_num_iters=200, database_size_limit=200, verbose=True)
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print("Finished the training")
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# plot underlying GMM
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gmm = kmp.reference_probability_distribution
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plot_gmm(gmm, dims=[1, 2], X=X.reshape(-1, 3), label=True, title='Underlying trained GMM', option=1, xlim=xlim,
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ylim=ylim)
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plt.show()
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# predict with GMR
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gaussians = []
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for t in time_linspace:
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g = gmm.condition(t, idx_out=[1, 2], idx_in=0).approximate_by_single_gaussian()
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gaussians.append(g)
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# plot figures for GMR
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plot_gmr(time_linspace, gaussians=gaussians, xlim=xlim, ylim=ylim, suptitle='GMR')
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# predict with the KMP
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gaussians = []
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for t in time_linspace:
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g = kmp.predict_proba(t, return_gaussian=True)
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gaussians.append(g)
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# plot figures for KMP
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plot_gmr(time_linspace, gaussians=gaussians, xlim=xlim, ylim=ylim, suptitle='KMP')
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plt.show()
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