From da56544f7299d87eaa464d86d8c9ee75b8c206e7 Mon Sep 17 00:00:00 2001
From: raccoonliukai <903896015@qq.com>
Date: Mon, 2 Sep 2019 22:04:23 +0800
Subject: [PATCH] feat(encoder): add PCAEncoder for incremental pca training

---
 gnes/encoder/numeric/pca.py | 36 ++++++++++++++++++++++++++++++++++++
 1 file changed, 36 insertions(+)

diff --git a/gnes/encoder/numeric/pca.py b/gnes/encoder/numeric/pca.py
index 970730d5..693a1d62 100644
--- a/gnes/encoder/numeric/pca.py
+++ b/gnes/encoder/numeric/pca.py
@@ -20,6 +20,42 @@
 from ...helper import get_perm, batching, get_optimal_sample_size, train_required
 
 
+class PCAEncoder(BaseNumericEncoder):
+    batch_size = 2048
+
+    def __init__(self, output_dim: int, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.output_dim = output_dim
+        self.pca_components = None
+        self.mean = None
+
+    def post_init(self):
+        from sklearn.decomposition import IncrementalPCA
+        self.pca = IncrementalPCA(n_components=self.output_dim)
+
+    @batching
+    def train(self, vecs: np.ndarray, *args, **kwargs) -> None:
+        num_samples, num_dim = vecs.shape
+        if self.output_dim > num_samples:
+            if self.mean.size:
+                return
+            else:
+                raise ValueError('training PCA requires at least %d points, but %d was given' % (self.output_dim, num_samples))
+
+        assert self.output_dim < num_dim, 'PCA output dimension should < data dimension, received (%d, %d)' % (
+            self.output_dim, num_dim)
+
+        self.pca.partial_fit(vecs)
+
+        self.pca_components = np.transpose(self.pca.components_)
+        self.mean = self.pca.mean_
+
+    @train_required
+    @batching
+    def encode(self, vecs: np.ndarray, *args, **kwargs) -> np.ndarray:
+        return np.matmul(vecs - self.mean, self.pca_components)
+
+
 class PCALocalEncoder(BaseNumericEncoder):
     batch_size = 2048