matrix-vector

app.py

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+import gradio as gr
+import numpy as np
+import matplotlib.pyplot as plt
+from io import BytesIO
+from PIL import Image
+def matrix_vector_multiplication_visualization(matrix, vector):
+    try:
+        # Parse inputs
+        matrix = np.array([[float(x) for x in row.split(",")] for row in matrix.split(";")])
+        vector = np.array([float(x) for x in vector.split(",")])
+        
+        # Ensure the matrix is 2x2 and the vector is 2D
+        if matrix.shape != (2, 2):
+            return "Error: Matrix must be 2x2.", None
+        if vector.shape != (2,):
+            return "Error: Vector must be 2D.", None
+        
+        # Perform matrix-vector multiplication
+        transformed_vector = np.dot(matrix, vector)
+        
+        # Create a grid for visualization
+        x = np.linspace(-1, 1, 10)
+        y = np.linspace(-1, 1, 10)
+        X, Y = np.meshgrid(x, y)
+        grid = np.vstack([X.flatten(), Y.flatten()])
+        transformed_grid = np.dot(matrix, grid).reshape(2, -1, 10)
+
+        # Create the plot
+        fig, ax = plt.subplots(figsize=(6, 6))
+        
+        # Plot the grid before and after transformation
+        for i in range(grid.shape[1]):
+            ax.plot([grid[0, i], transformed_grid[0, i]], [grid[1, i], transformed_grid[1, i]], 
+                    color="gray", linewidth=0.5, alpha=0.7)
+        
+        # Plot the original vector
+        ax.quiver(0, 0, vector[0], vector[1], angles="xy", scale_units="xy", scale=1, color="red", label="Original Vector")
+        
+        # Plot the transformed vector
+        ax.quiver(0, 0, transformed_vector[0], transformed_vector[1], angles="xy", scale_units="xy", scale=1, color="blue", label="Transformed Vector")
+        
+        # Plot settings
+        ax.axhline(0, color='black', linewidth=0.5)
+        ax.axvline(0, color='black', linewidth=0.5)
+        ax.set_xlim(-2, 2)
+        ax.set_ylim(-2, 2)
+        ax.set_aspect('equal')
+        ax.grid(True)
+        ax.legend()
+        ax.set_title("Matrix-Vector Multiplication Visualization")
+        
+        # Save the plot to a BytesIO object
+        buf = BytesIO()
+        plt.savefig(buf, format="png")
+        buf.seek(0)
+        plt.close(fig)
+        
+        return f"Transformed Vector: {transformed_vector.tolist()}", Image.open(buf)
+    except Exception as e:
+        return f"Error: {str(e)}", None
+
+# Create the Gradio app
+with gr.Blocks() as app:
+    gr.Markdown("## Matrix-Vector Multiplication Visualization")
+    gr.Markdown("""
+    - Enter a **2x2 matrix** as `a,b;c,d` (rows separated by semicolons).
+    - Enter a **2D vector** as `x,y`.
+    - See the original vector (red), transformed vector (blue), and grid transformation.
+    """)
+
+    with gr.Row():
+        matrix_input = gr.Textbox(label="Matrix (2x2, e.g., 1,0;0,1)", placeholder="e.g., 1,0;0,1")
+        vector_input = gr.Textbox(label="Vector (2D, e.g., 1,1)", placeholder="e.g., 1,1")
+    
+    output_text = gr.Textbox(label="Result")
+    output_image = gr.Image(label="Visualization")
+    
+    calculate_button = gr.Button("Visualize")
+    calculate_button.click(
+        fn=matrix_vector_multiplication_visualization,
+        inputs=[matrix_input, vector_input],
+        outputs=[output_text, output_image]
+    )
+
+app.launch()

requirements.txt

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+
+# Usage: pip install -r requirements.txt
+ 
+# Base ----------------------------------------
+hydra-core>=1.2.0
+matplotlib>=3.2.2
+numpy>=1.18.5
+opencv-python>=4.1.1
+Pillow>=7.1.2
+PyYAML>=5.3.1
+requests>=2.23.0
+scipy>=1.4.1
+tqdm>=4.64.0
+ 
+# Logging -------------------------------------
+tensorboard>=2.4.1
+# clearml
+# comet
+ 
+# Plotting ------------------------------------
+pandas>=1.1.4
+seaborn>=0.11.0
+ 
+# Export --------------------------------------
+# coremltools>=6.0  # CoreML export
+# onnx>=1.12.0  # ONNX export
+# onnx-simplifier>=0.4.1  # ONNX simplifier
+# nvidia-pyindex  # TensorRT export
+# nvidia-tensorrt  # TensorRT export
+# scikit-learn==0.19.2  # CoreML quantization
+# tensorflow>=2.4.1  # TF exports (-cpu, -aarch64, -macos)
+# tensorflowjs>=3.9.0  # TF.js export
+# openvino-dev  # OpenVINO export
+ 
+# Extras --------------------------------------
+ipython  # interactive notebook
+psutil  # system utilization
+thop>=0.1.1  # FLOPs computation
+# albumentations>=1.0.3
+# pycocotools>=2.0.6  # COCO mAP
+# roboflow
+ 
+# HUB -----------------------------------------
+GitPython>=3.1.24