From 42bffbcaf1864969b8c994ef2b17e7e11ec83f71 Mon Sep 17 00:00:00 2001
From: Precreator <1689487228@qq.com>
Date: Thu, 26 Jun 2025 17:48:19 +0800
Subject: [PATCH] =?UTF-8?q?=E5=AE=8C=E6=88=90=E4=BA=86tensor.cc=E7=9A=84?=
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---
 cc/tensor/tensor.cc | 55 +++++++++++++++++++++++++++++++++++++++++++--
 1 file changed, 53 insertions(+), 2 deletions(-)

diff --git a/cc/tensor/tensor.cc b/cc/tensor/tensor.cc
index bedf5ff..43cbff4 100644
--- a/cc/tensor/tensor.cc
+++ b/cc/tensor/tensor.cc
@@ -16,11 +16,17 @@ std::shared_ptr<Tensor> Tensor::transpose() {
     std::vector<float> transposed_data(size);
 
     // 请在这里写转置的代码
-
+    for(std::size_t i=0;i<cols;i++)
+    {
+        for(std::size_t j=0;j<rows;j++)
+        {
+            transposed_data[i*cols+j]=data[j*rows+i];//
+        }
+    }
     // 请阅读关于Tensor的定义，在这里创建一个新的Tensor
     // 注意，要使用shared_ptr哦！
     
-    return std::make_shared<Tensor>(new_shape);
+    return std::make_shared<Tensor>(new_shape,transposed_data);
 }
 
 
@@ -50,6 +56,51 @@ std::shared_ptr<Tensor> argmax(const std::shared_ptr<Tensor>& tensor, int axis)
     auto result = std::make_shared<Tensor>(output_shape);
     // 这个问题似乎有点难，所以我们决定给你送点分。一个简单的办法是分axis为0还是为1来进行讨论，反正我们已经把问题简化为了，在一个二维的tensor里面，找到每一行或者每一列的最大值，并输出一个一维的tensor。
     // 补全这里的代码。
+       size_t rows=tensor->shape[0];
+       size_t cols=tensor->shape[1];
+       if(axis==0)
+       {
+        output_shape={1,cols};
+       }
+       else if(axis==1)
+       {
+        output_shape={rows,1};
+       }
+       if(axis==0)//qiu lie xiang liang 
+       {
+            for(size_t j=0;j<cols;j++)
+            {
+                float maxx=0;
+                size_t maxx_id=0;//ji lu ID
+                for(size_t i=0;i<rows;i++)
+                {
+                    if(tensor->data[i*cols+j]>maxx) 
+                    {
+                        maxx=tensor->data[i*cols+j];
+                        maxx_id=i;
+                    }
+                }
+                result->data[j] = static_cast<float>(maxx_id);
+            }
+       }
+       else
+       {
+            for(size_t i=0;i<rows;i++)
+            {
+               float maxx=0;
+                size_t maxx_id=0;
+                for (size_t j=0;j<cols;j++)
+                {
+                    if(maxx<tensor->data[i*cols+j])
+                    {
+                        maxx=tensor->data[i*cols+j];
+                        maxx_id=j;
+                    }
+                }
+                result->data[i] = static_cast<float>(maxx_id);
+            }
+       }
+
     return result;
 }