Develop shape_consistency

bayesml/_check.py

@@ -1,6 +1,8 @@
 # Code Author
 # Yuta Nakahara <yuta.nakahara@aoni.waseda.jp>
 # Yuji Iikubo <yuji-iikubo.8@fuji.waseda.jp>
+# Yasushi Esaki <esakiful@gmail.com>
+# Jun Nishikawa <jun.b.nishikawa@gmail.com>
 import numpy as np
 
 _EPSILON = np.sqrt(np.finfo(np.float64).eps)
@@ -100,6 +102,22 @@ def pos_def_sym_mat(val,val_name,exception_class):
         pass
     raise(exception_class(val_name + " must be a positive definite symmetric 2-dimensional numpy.ndarray."))
 
+def sym_mats(val,val_name,exception_class):
+    if type(val) is np.ndarray:
+        if val.ndim >= 2 and val.shape[-1] == val.shape[-2]:
+            if np.allclose(val, np.swapaxes(val,-1,-2)):
+                return val
+    raise(exception_class(val_name + " must be a symmetric 2-dimensional numpy.ndarray."))
+
+def pos_def_sym_mats(val,val_name,exception_class):
+    sym_mats(val,val_name,exception_class)
+    try:
+        np.linalg.cholesky(val)
+        return val
+    except np.linalg.LinAlgError:
+        pass
+    raise(exception_class(val_name + " must be a positive definite symmetric 2-dimensional numpy.ndarray."))
+
 def float_(val,val_name,exception_class):
     if np.issubdtype(type(val),np.floating):
         return val
@@ -163,6 +181,14 @@ def float_vec_sum_1(val,val_name,exception_class):
             return val
     raise(exception_class(val_name + " must be a 1-dimensional numpy.ndarray, and the sum of its elements must equal to 1."))
 
+def float_vecs_sum_1(val,val_name,exception_class):
+    if type(val) is np.ndarray:
+        if np.issubdtype(val.dtype,np.integer) and val.ndim >= 1 and np.all(np.abs(np.sum(val, axis=-1) - 1.) <= _EPSILON):
+            return val.astype(float)
+        if np.issubdtype(val.dtype,np.floating) and val.ndim >= 1 and np.all(np.abs(np.sum(val, axis=-1) - 1.) <= _EPSILON):
+            return val
+    raise(exception_class(val_name + " must be a numpy.ndarray whose ndim >= 1, and the sum along the last dimension must equal to 1."))
+
 def int_(val,val_name,exception_class):   
     if np.issubdtype(type(val),np.integer):
         return val
@@ -189,3 +215,9 @@ def onehot_vecs(val,val_name,exception_class):
         if np.issubdtype(val.dtype,np.integer) and val.ndim >= 1 and np.all(val >= 0) and np.all(val.sum(axis=-1)==1):
             return val
     raise(exception_class(val_name + " must be a numpy.ndarray whose dtype is int and whose last axis constitutes one-hot vectors."))
+
+def shape_consistency(val: int, val_name: str, correct: int, correct_name: str, exception_class):
+    if val != correct:
+        message = (f"{val_name} must coincide with {correct_name}: "
+                   + f"{val_name} = {val}, {correct_name} = {correct}")
+        raise(exception_class(message))