From d0fc2bd3e92b9fd12d26fe4f801c5206c71e6765 Mon Sep 17 00:00:00 2001
From: Daouzli A <daouzli@gmail.com>
Date: Fri, 19 Jan 2018 22:50:52 +0100
Subject: [PATCH] update prepare and compute output for using given inputs

---
 mlp.py | 56 +++++++++++++++++++++++++++++++++++++++++---------------
 1 file changed, 41 insertions(+), 15 deletions(-)

diff --git a/mlp.py b/mlp.py
index 34a62d6..cfd5b5a 100644
--- a/mlp.py
+++ b/mlp.py
@@ -229,7 +229,7 @@ class MultiLayerPerceptron(object):
             assert(len(X) == m)
             self._X = np.matrix(X).T
         else:
-            print(X.shape, self._n[0], m)
+            #print(X.shape, self._n[0], m)
             assert(X.shape == (self._n[0], m))
             self._X = X
         self._m = m
@@ -268,14 +268,37 @@ class MultiLayerPerceptron(object):
         self.set_all_input_examples(X, m)
         self.set_all_expected_output_examples(Y, m)
 
-    def prepare(self):
+    def prepare(self, X=None, m=None, force=False):
         """Prepare network for propagation"""
-        if self._prepared == False:
-            self._prepared = True
-            assert(self._X is not None)
-            assert(self._m > 0)
-            m = self._m
-            self._A = [self._X]
+        if X is not None:
+            force = True
+        #print("sforce,prep =", force, self._prepared)
+        if force or self._prepared == False:
+            if not force:
+                self._prepared = True
+            if X is None:
+                assert(self._X is not None)
+                X = self._X
+                if m is None:
+                    m = self._m
+                if m == 0:
+                    if type(X) is list:
+                        m = 1
+                    else:
+                        m = X.shape[1]
+            else:
+                if m is None:
+                    if type(X) is list:
+                        m = 1
+                    else:
+                        m = X.shape[1]
+                if type(X) is list:
+                    X = np.array(X).reshape(len(X), 1)
+            if m is None:
+                assert(self._m > 0)
+                m = self._m
+            #print("m prep =", m)
+            self._A = [X]
             self._A += [np.empty((self._n[l+1], m)) for l in range(self._L)]
             self._Z = [None] + [np.empty((self._n[l+1], m)) for l in range(self._L)]
 
@@ -291,7 +314,7 @@ class MultiLayerPerceptron(object):
             self._A[l] = self._g[l]["function"](self._Z[l])
         return self._A[self._L]
 
-    def compute_outputs(self, X=None):
+    def compute_outputs(self, X=None, m=None):
         """Compute outputs with forward propagation.
         Note: if no input provided, then the input should have been set using
         either `set_all_input_examples()` or `set_all_training_examples()`.
@@ -301,12 +324,15 @@ class MultiLayerPerceptron(object):
 
         """
         if X is not None:
-            if type(X) is list:
-                m = len(X)
-            else:
-                m = X.shape[1]
-            self.set_all_input_examples(X, m)
-        self.prepare()
+            if m is None:
+                if type(X) is list:
+                    m = 1
+                else:
+                    m = X.shape[1]
+            print("lenX,m",len(X),m)
+            self.prepare(X, m)
+        else:
+            self.prepare()
         self.propagate()
         return self._A[self._L]