LESSON 46 OF 52 · CHAPTER 06
Matrix Targets
tile, matrix-vector scores, argmax and onehot
05-matrix-targets.srp
bind scores = matvec(tile([1.0, 0.0, 0.0, 2.0, -1.0, 1.0], 3, 2), [2.0, 1.0]);
print(onehot(len(scores), argmax(scores)));
stdout
[1.0, 0.0, 0.0]
Write what you expect this program to print. Check prediction for a line-level hint without revealing the answer, or reveal the verified output when ready. This checks displayed text only; it does not run Scarp or save your input.
UNDERSTAND THE CODE
Explanation & practice
What this example teaches
tile, matrix-vector scores, argmax and onehot
Expected behavior
[1.0, 0.0, 0.0]
Practice: Read the source before looking at the output. Predict the values and their order, then use the pinned compiler to execute this lesson and compare the exact fixture. Identify which operation produces the result.
मैट्रिक्स स्कोर से वन-हॉट लक्ष्य
tile से 3×2 का मैट्रिक्स और matvec से तीन स्कोर निकाले जाते हैं। argmax अधिकतम स्कोर का इंडेक्स चुनता है और onehot उसी इंडेक्स पर 1 रखता है। यह केवल अंकगणितीय उदाहरण है।
आउटपुट: [1.0, 0.0, 0.0]।
अभ्यास: 2.0 और 1.0 फीचर को मैट्रिक्स की तीन पंक्तियों से गुणा करके स्कोर निकालें।
Explanations are sourced from the project's English chapter guide and हिन्दी अध्याय. The Scarp program and output are unchanged.
LEARNING PROGRESS
Keep your place
Mark this lesson complete when you are ready.
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TRY IT LOCALLY
Verify, then execute
Use the pinned compiler from the course root. Keep any imported _support files next to the source.
scarp verify examples/06-ai-foundations/05-matrix-targets.srp
scarp exec examples/06-ai-foundations/05-matrix-targets.srpThe website does not run code. Read the CLI guide ↗
Pinned compiler: Scarp v0.0.6-alpha.3
· ceff07d9014f. No simulated output.