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@@ 542-546 (lines=5) @@
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| 539 |
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| 540 |
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for neighbor_index in self._neighbors[index]:
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| 541 |
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distance = self._sqrt_distances[index][neighbor_index]
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| 542 |
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if (distance < self._local_radius):
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| 543 |
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influence = math.exp( -( distance / (2.0 * self._local_radius) ) );
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| 544 |
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| 545 |
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for i in range(dimension):
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| 546 |
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self._weights[neighbor_index][i] = self._weights[neighbor_index][i] + self._learn_rate * influence * (x[i] - self._weights[neighbor_index][i]);
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| 547 |
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| 548 |
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| 549 |
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def train(self, data, epochs, autostop = False):
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@@ 530-534 (lines=5) @@
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| 527 |
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for neuron_index in range(self._size):
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| 528 |
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distance = self._sqrt_distances[index][neuron_index];
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| 529 |
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| 530 |
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if (distance < self._local_radius):
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| 531 |
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influence = math.exp( -( distance / (2.0 * self._local_radius) ) );
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| 532 |
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| 533 |
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for i in range(dimension):
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| 534 |
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self._weights[neuron_index][i] = self._weights[neuron_index][i] + self._learn_rate * influence * (x[i] - self._weights[neuron_index][i]);
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| 535 |
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else:
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| 537 |
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for i in range(dimension):
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annoviko /
pyclustering
