pdfcoffee

Unsupervised Learning
Then there are some more helper functions to find the winner and generate a 2D
lattice of neurons, and a function to map input vectors to the corresponding neurons
in the 2D lattice:
def winner(self, x):
idx = self.WTU_idx,self.WTU_loc
return idx
def _neuron_location(self,m,n):
"""
Function to generate the 2D lattice of neurons
"""
for i in range(m):
for j in range(n):
yield np.array([i,j])
def get_centroids(self):
"""
Function to return a list of 'm' lists, with each inner
list containing the 'n' corresponding centroid locations as 1-D
NumPy arrays.
"""
if not self._learned:
raise ValueError("SOM not trained yet")
return self._centroid_grid
def map_vects(self, X):
"""
Function to map each input vector to the relevant
neuron in the lattice
"""
if not self._learned:
raise ValueError("SOM not trained yet")
to_return = []
for vect in X:
min_index = min([i for i in range(len(self._Wts))],
key=lambda x: np.linalg.norm(vect -
self._Wts[x]))
to_return.append(self._locations[min_index])
return to_return
[ 390 ]