From a3a6130254c0f3e6590d5c2a95afa1baefe7a24f Mon Sep 17 00:00:00 2001 From: Wolves Date: Fri, 14 Jun 2024 20:24:09 +0800 Subject: [PATCH] 230614 --- README.md | 11 +- linear regression/liner regression.ipynb | 5234 ++++++++++++++++++++++ 2 files changed, 5241 insertions(+), 4 deletions(-) create mode 100644 linear regression/liner regression.ipynb diff --git a/README.md b/README.md index 5380cb9..2e70f8c 100644 --- a/README.md +++ b/README.md @@ -15,15 +15,18 @@ conda install pytorch::pytorch torchvision torchaudio -c pytorch -y pip install -r requirements.txt ``` -## MAC +## linux ```shell -# 安装 pytorch v1.12版本已经正式支持了用于mac m1芯片gpu加速的mps后端 conda install pytorch torchvision torchaudio pytorch-cuda=12.1 -c pytorch -c nvidia pip install -r requirements.txt ``` -## gpt4free +## Jupyter +```shell +# env +pip install jupyter ``` -pip install -U g4f[all] +```shell +jupyter notebook ``` \ No newline at end of file diff --git a/linear regression/liner regression.ipynb b/linear regression/liner regression.ipynb new file mode 100644 index 0000000..bc0cf52 --- /dev/null +++ b/linear regression/liner regression.ipynb @@ -0,0 +1,5234 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "gpuType": "T4", + "mount_file_id": "1kCjTIM3v5mm8NXbKPLmlRKl7twd9xU94", + "authorship_tag": "ABX9TyOLIwV8MmfP7m3yiBMZyYF+" + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + }, + "accelerator": "GPU" + }, + "cells": [ + { + "cell_type": "code", + "source": [ + "!pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121\n", + "!pip install numpy pandas pillow matplotlib" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "d5k_A4uC6e3f", + "executionInfo": { + "status": "ok", + "timestamp": 1718270822835, + "user_tz": -480, + "elapsed": 94497, + "user": { + "displayName": "wolves li", + "userId": "01742783151929573764" + } + }, + "outputId": "15326d01-d0fe-400f-bdc9-915e4ab959b8" + }, + "execution_count": 9, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Looking in indexes: https://download.pytorch.org/whl/cu121\n", + "Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (2.3.0+cu121)\n", + "Requirement already satisfied: torchvision in /usr/local/lib/python3.10/dist-packages (0.18.0+cu121)\n", + "Requirement already satisfied: torchaudio in /usr/local/lib/python3.10/dist-packages (2.3.0+cu121)\n", + "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch) (3.14.0)\n", + "Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.10/dist-packages (from torch) (4.12.2)\n", + "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch) (1.12.1)\n", + "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch) 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totalError = 0\n", + " N = float(len(points))\n", + " for i in range(len(points)):\n", + " x = points[i][0]\n", + " y = points[i][1]\n", + " totalError += (y - (w * x + b)) ** 2\n", + " return totalError / N\n", + "\n", + "def step_gradient(b_current, w_current, points, learningRate):\n", + " b_gradient = torch.tensor(0.0, device=points.device)\n", + " w_gradient = torch.tensor(0.0, device=points.device)\n", + " N = float(len(points))\n", + " for i in range(len(points)):\n", + " x = points[i][0]\n", + " y = points[i][1]\n", + " b_gradient += -(2 / N) * (y - (w_current * x + b_current))\n", + " w_gradient += -(2 / N) * x * (y - (w_current * x + b_current))\n", + " new_b = b_current - (learningRate * b_gradient)\n", + " new_w = w_current - (learningRate * w_gradient)\n", + " return [new_b, new_w]\n", + "\n", + "def gradient_descent_runner(points, starting_b, starting_w, learningRate, num_iterations):\n", + " b = torch.tensor(starting_b, device=points.device)\n", + " w = torch.tensor(starting_w, device=points.device)\n", + " for i in range(num_iterations):\n", + " print(\"round:\" + str(i))\n", + " b, w = step_gradient(b, w, points, learningRate)\n", + " return [b, w]\n", + "\n", + "def run():\n", + " points_np = np.genfromtxt(\"/content/drive/MyDrive/PT/data1.csv\", delimiter=',').astype(np.float64)\n", + " points = torch.tensor(points_np, device='cuda')\n", + " learning_rate = 0.0001\n", + " initial_b = 0.0\n", + " initial_w = 0.0\n", + " num_iterations = 100000\n", + " [b, w] = gradient_descent_runner(points, initial_b, initial_w, learning_rate, num_iterations)\n", + " print(\"After gradient descent at b={0}, w={1}, error={2}\".format(b.item(), w.item(),\n", + " compute_error_for_line_given_points(b, w, points)))\n", + " return b.item(), w.item()\n", + "\n", + "# 运行线性回归\n", + "final_b, final_w = run()\n", + "\n", + "# 绘制图像\n", + "points_np = np.genfromtxt(\"/content/drive/MyDrive/PT/data1.csv\", delimiter=',').astype(np.float64)\n", + "x = points_np[:, 0]\n", + "y = points_np[:, 1]\n", + "\n", + "x_range = np.linspace(min(x), max(x), 100)\n", + "y_pred = final_w * x_range + final_b\n", + "\n", + "plt.figure(figsize=(8, 6))\n", + "plt.scatter(x, y, color='blue', label='Original data')\n", + "plt.plot(x_range, y_pred, color='red', label='Fitted line')\n", + "plt.xlabel('X')\n", + "plt.ylabel('Y')\n", + "plt.title('Fitting a line to random data')\n", + "plt.legend()\n", + "plt.grid(True)\n", + "plt.savefig('/content/drive/MyDrive/PT/print1.png')\n", + "plt.show()\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 0 + }, + "id": "4uVNEBT96_HK", + "executionInfo": { + "status": "ok", + "timestamp": 1718272497978, + "user_tz": -480, + "elapsed": 1187345, + "user": { + "displayName": "wolves li", + "userId": "01742783151929573764" + } + }, + "outputId": "7f495cb6-2e31-4569-f0f2-38587cb48f89" + }, + "execution_count": 17, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + 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"round:99938\n", + "round:99939\n", + "round:99940\n", + "round:99941\n", + "round:99942\n", + "round:99943\n", + "round:99944\n", + "round:99945\n", + "round:99946\n", + "round:99947\n", + "round:99948\n", + "round:99949\n", + "round:99950\n", + "round:99951\n", + "round:99952\n", + "round:99953\n", + "round:99954\n", + "round:99955\n", + "round:99956\n", + "round:99957\n", + "round:99958\n", + "round:99959\n", + "round:99960\n", + "round:99961\n", + "round:99962\n", + "round:99963\n", + "round:99964\n", + "round:99965\n", + "round:99966\n", + "round:99967\n", + "round:99968\n", + "round:99969\n", + "round:99970\n", + "round:99971\n", + "round:99972\n", + "round:99973\n", + "round:99974\n", + "round:99975\n", + "round:99976\n", + "round:99977\n", + "round:99978\n", + "round:99979\n", + "round:99980\n", + "round:99981\n", + "round:99982\n", + "round:99983\n", + "round:99984\n", + "round:99985\n", + "round:99986\n", + "round:99987\n", + "round:99988\n", + "round:99989\n", + "round:99990\n", + "round:99991\n", + "round:99992\n", + "round:99993\n", + "round:99994\n", + "round:99995\n", + "round:99996\n", + "round:99997\n", + "round:99998\n", + "round:99999\n", + "After gradient descent at b=2.8940329551696777, w=1.339672565460205, error=0.1857876382824373\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ] + } + ] +}