Step 9: Celebrate your artistic and analytical achievement!
"Why did the PCA plot go to the party alone? Because it couldn't find any correlated variables!"
Javascript Code
// Title: "PCA Plot: Dimensionality Reduction in Action"
// Step 1: Set up the canvas
// Step 2: Define the data points
const observations = [];
for (let i = 0; i < 200; i++) {
const variables = [];
for (let j = 0; j < 25; j++) {
}
}
// Step 3: Perform PCA (Principal Component Analysis)
const pca = new PCA(observations);
const result = pca.predict(2); // Reduce the dimensionality to 2 for plotting
// Step 4: Set up the plot
const plotSize = 400; // Size of the plot area within the canvas
const plotOriginX = (512 - plotSize) / 2; // X-coordinate of the top-left corner of the plot area
const plotOriginY = (512 - plotSize) / 2; // Y-coordinate of the top-left corner of the plot area
// Step 5: Draw the axes
ctx.beginPath();
ctx.moveTo(plotOriginX, plotOriginY + plotSize); // Bottom-left corner of the plot area
ctx.lineTo(plotOriginX, plotOriginY); // Top-left corner of the plot area
ctx.lineTo(plotOriginX + plotSize, plotOriginY); // Top-right corner of the plot area
ctx.strokeStyle = 'black';
ctx.lineWidth = 2;
ctx.stroke();
// Step 6: Plot the data points
ctx.beginPath();
for (let i = 0; i < result.length; i++) {
const x = result[i][0] * plotSize + plotOriginX; // Scale and shift the x-coordinate
const y = result[i][1] * plotSize + plotOriginY; // Scale and shift the y-coordinate
ctx.moveTo(x, y);
ctx.arc(x, y, 2, 0, Math.PI * 2); // Draw a small circle for each data point
}
ctx.fillStyle = 'blue';
ctx.fill();
// Step 7: Add labels
ctx.font = '12px Arial';
ctx.fillStyle = 'black';
ctx.fillText('PC1', plotOriginX + plotSize - 20, plotOriginY - 5); // Label for the x-axis (PC1)
ctx.fillText('PC2', plotOriginX - 20, plotOriginY + 15); // Label for the y-axis (PC2)
// Step 8: Explain the plot
ctx.font = '16px Arial';
ctx.fillText('PCA Plot: Dimensionality Reduction in Action', 10, 30); // Title of the plot
ctx.fillText('This plot represents the result of applying PCA', 10, 60);
ctx.fillText('to a dataset with 200 observations and 25 variables.', 10, 90);
ctx.fillText('PCA allows us to reduce the dimensionality of the', 10, 120);
ctx.fillText('data while preserving the most important information.', 10, 150);
ctx.fillText('In this plot, each data point is represented by a', 10, 180);
ctx.fillText('small blue circle, and the x and y axes correspond', 10, 210);
ctx.fillText('to the first and second principal components (PC1 and PC2).', 10, 240);
ctx.fillText('By visualizing the data in this reduced dimensional', 10, 270);
ctx.fillText('space, we can gain insights into its underlying structure', 10, 300);
ctx.fillText('and identify patterns or clusters that may not be', 10, 330);
ctx.fillText('apparent in the original high-dimensional space.', 10, 360);
// Step 9: Celebrate your artistic and analytical achievement!
// "Why did the PCA plot go to the party alone? Because it couldn't find any correlated variables!"
AI
Model: gpt-3.5-turbo-16k-0613
Time: 35 seconds
Prompt Tokens: 191
Completion Tokens: 854
Total Token Cost: 1045
balloon party
beautiful beach
cute puppy dog
happy cat
house