machinelearning

commit e73bbe68068fc543da8bdb0953cd5051b4d9fb63
parent e9171b0c875e25f7caa2aa45a6eb938963ff9744
Author: Andrew <andrewlaack1@gmail.com>
Date:   Thu, 14 Nov 2024 20:16:32 -0600

revised mat mul

Diffstat:
M
nnFromScratch/matMul(DP).ipynb
 | 
38
++++++++++++++++++++++++++++++++------

1 file changed, 32 insertions(+), 6 deletions(-)
diff --git a/nnFromScratch/matMul(DP).ipynb b/nnFromScratch/matMul(DP).ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 169,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -24,17 +24,17 @@
     "\n",
     "# mat mul\n",
     "def naiveMatMul(x,y):\n",
-    "    ret = np.zeros(shape=(len(x), len(y)))\n",
+    "    ret = np.zeros(shape=(len(x), len(y[0])))\n",
     "\n",
     "    for z in range(0,len(x)):\n",
-    "        for i in range(0, len(y)):\n",
+    "        for i in range(0, len(y[0])):\n",
     "            ret[z][i] = naiveVectorDotProduct(x[z], y[:,i])\n",
     "    return ret"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 92,
+   "execution_count": 170,
    "metadata": {},
    "outputs": [
     {
@@ -55,7 +55,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": 171,
    "metadata": {},
    "outputs": [
     {
@@ -81,7 +81,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 91,
+   "execution_count": 172,
    "metadata": {},
    "outputs": [
     {
@@ -112,6 +112,32 @@
     "print(\"First: \\n\" + str(X@y))\n",
     "print(\"Second: \\n\" + str(naiveMatMul(X,y)))"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 190,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 190,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x = np.random.random((5,30))\n",
+    "y = np.random.random((30,2))\n",
+    "\n",
+    "prod = (x @ y).astype(np.uint)\n",
+    "product = naiveMatMul(x,y).astype(np.uint)\n",
+    "res = product == prod\n",
+    "res.all()"
+   ]
   }
  ],
  "metadata": {