From 18074c08ca3e9ab01f385d6794cfcb6b5fff2c73 Mon Sep 17 00:00:00 2001
From: JustGlowing <giuseppe.vettigli@hivehome.com>
Date: Thu, 22 Aug 2024 13:45:13 +0100
Subject: [PATCH] fixed points added

---
 minisom.py | 49 +++++++++++++++++++++++++++++++++++++++++++++++--
 1 file changed, 47 insertions(+), 2 deletions(-)

diff --git a/minisom.py b/minisom.py
index c85a8b3..5a685c0 100644
--- a/minisom.py
+++ b/minisom.py
@@ -448,7 +448,8 @@ def pca_weights_init(self, data):
                                       c2*pc[pc_order[1]]
 
     def train(self, data, num_iteration,
-              random_order=False, verbose=False, use_epochs=False):
+              random_order=False, verbose=False,
+              use_epochs=False, fixed_points=None):
         """Trains the SOM.
 
         Parameters
@@ -473,6 +474,12 @@ def train(self, data, num_iteration,
             If True the SOM will be trained for num_iteration epochs.
             In one epoch the weights are updated len(data) times and
             the learning rate is constat throughout a single epoch.
+
+        fixed_points : dict (default=None)
+            A dictionary k : (c_1, c_2), that will force the
+            training algorithm to use the neuron with coordinates
+            (c_1, c_2) as winner for the samples k instead of
+            the best matching unit.
         """
         self._check_iteration_number(num_iteration)
         self._check_input_len(data)
@@ -488,9 +495,31 @@ def get_decay_rate(iteration_index, data_len):
         else:
             def get_decay_rate(iteration_index, data_len):
                 return int(iteration_index)
+
+        if fixed_points:
+            for k in fixed_points.keys():
+                if not isinstance(k, int):
+                    raise TypeError(f'fixed points indexes must ' + 
+                                     'be integers.')
+                if k >= len(data) or k < 0:
+                    raise ValueError(f'an index of a fixed point ' +
+                                     'cannot be grater than len(data)' +
+                                     ' or less than 0.')
+                if fixed_points[k][0] >= self._weights.shape[0] or \
+                   fixed_points[k][1] >= self._weights.shape[1]:
+                    raise ValueError(f'coordinates for fixed point' +
+                                     ' are out of boundaries.')
+                if fixed_points[k][0] < 0 or \
+                   fixed_points[k][1] < 0:
+                    raise ValueError(f'coordinates cannot be negative.')
+        else:
+            fixed_points = {}
+
         for t, iteration in enumerate(iterations):
             decay_rate = get_decay_rate(t, len(data))
-            self.update(data[iteration], self.winner(data[iteration]),
+            self.update(data[iteration],
+                        fixed_points.get(iteration,
+                                         self.winner(data[iteration])),
                         decay_rate, num_iteration)
         if verbose:
             print('\n quantization error:', self.quantization_error(data))
@@ -910,6 +939,22 @@ def test_train_use_epochs(self):
         som.train(data, 10, use_epochs=True)
         assert q1 > som.quantization_error(data)
 
+    def test_train_fixed_points(self):
+        som = MiniSom(5, 5, 2, sigma=1.0, learning_rate=0.5, random_seed=1)
+        data = array([[4, 2], [3, 1]])
+        som.train(data, 10, fixed_points={0: (0, 0)})
+        with self.assertRaises(ValueError):
+            som.train(data, 10, fixed_points={0: (5, 0)})
+        with self.assertRaises(ValueError):
+            som.train(data, 10, fixed_points={2: (0, 0)})
+        with self.assertRaises(ValueError):
+            som.train(data, 10, fixed_points={0: (-1, 0)})
+        with self.assertRaises(ValueError):
+            som.train(data, 10, fixed_points={-1: (0, 0)})
+        with self.assertRaises(TypeError):
+            som.train(data, 10, fixed_points={'oops': (0, 0)})
+        
+
     def test_use_epochs_variables(self):
         len_data = 100000
         num_epochs = 100