mirror of
https://github.com/WallabyLester/RBF-aPID-Controller.git
synced 2026-05-09 20:53:57 +08:00
Andru Liu
86 lines
2.7 KiB
Python
86 lines
2.7 KiB
Python
import numpy as np
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class RBFNetwork:
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""" Basic radial basis function (RBF) neural network class, numpy implementation.
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Contains input layer, one hidden layer with RBF activation, and linear output layer.
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RBF activation is a Gaussian.
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...
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Attributes
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----------
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input_dim : int
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The dimension of the RBF centers.
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n_centers : int
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The number of RBF centers.
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Methods
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-------
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gaussian(x, center):
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Forms the gaussian given the center and input.
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predict(x, center):
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Predicts from the model using the gaussian and saved weights.
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train(x, target):
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Train the RBF model on stored data.
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"""
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def __init__(self, input_dim, n_centers):
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""" Constructs distribution parameters and initializes weights.
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Parameters
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----------
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input_dim : int
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The dimension of the RBF centers.
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n_centers : int
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The number of RBF centers.
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"""
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self.input_dim = input_dim
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self.n_centers = n_centers
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self.centers = np.random.rand(n_centers, input_dim) # expected value
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self.sigma = 1.0 # variance
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self.weights = np.random.rand(n_centers)
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def gaussian(self, x, center):
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""" Find likelihood of x under Gaussian distribution centered at center with
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standard deviation sigma.
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Parameters
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----------
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x : ndarray[Any, dtype[float64]]
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The point in space to evaluate the Gaussian.
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center : ndarray[Any, dtype[float64]]
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Mean/center of Gaussian distribution.
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Returns
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-------
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Height of Gaussian curve at x.
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"""
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return np.exp(-np.linalg.norm(x - center) ** 2 / (2 * self.sigma ** 2))
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def predict(self, x):
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""" Prediction function of form dot(Activations, Weights).
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Parameters
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----------
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x : ndarray[Any, dtype[float64]]
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The point in space to evaluate the Gaussian.
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Returns
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-------
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Approximation of the target function.
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"""
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activations = np.array([self.gaussian(x, center) for center in self.centers])
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return np.dot(activations, self.weights)
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def train(self, x, target):
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""" Training function to adapt weights to known datapoints.
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Parameters
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----------
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x : ndarray[Any, dtype[float64]]
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The point in space to evaluate the Gaussian.
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target : float64
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Target data point.
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"""
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activations = np.array([self.gaussian(x, center) for center in self.centers])
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self.weights += 0.01 * (target - np.dot(activations, self.weights)) * activations |