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Classical Machine Learning

Predict the next letter's category

BOTIE

What is the probability that the next letter is a vowel or consonant? Apply the transition table to the current distribution. Rows describe the current category and columns describe the next, always ordered vowel then consonant.
Ready to test
0 passed · 0 failed · 1 not run

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Examples, experiments and workspace controls
Live values

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Context for this challenge

What is the probability that the next letter is a vowel or consonant? Apply the transition table to the current distribution. Rows describe the current category and columns describe the next, always ordered vowel then consonant.

The current letter may be a vowel or consonant. Botie must let each possible current category contribute through its own transition row.

Transpose the current-to-next transition table for matrix-vector multiplication, then apply the current distribution in vowel-then-consonant order.

Examples and results
Current vowel/consonant probabilities
  1. 01
  2. 10
Vowel/consonant transition rows
0.80.2
0.60.4
Required result
  1. 00.8
  2. 10.2
Try a fresh construction

This clears this draft, including its undo history. Earlier successful attempts stay recorded. Build again without opening hints to record an independent attempt.

Stuck? Inspect a worked construction

The tools below form one possible solution. You can assemble it, follow its signals, and undo that change to return to your own construction.

Assumptions and test precision

This is a small construction problem. The lesson explains the assumptions and the parts of the full method that this example leaves out. Numerical tests allow a difference of 0.001. Lists and structures must also have the right shape.

A known consonant under a new transition table

This check uses the pieces from “Mix transition rows using the current category shares”.

BOTIE

The current category is certainly a consonant. Predict the next vowel/consonant distribution using the new transition rows.
Current vowel/consonant probabilities
  1. 00
  2. 11
Vowel/consonant transition rows
0.250.75
0.90.1

Use a number, text, or an array matching the requested result. For a matrix, put each row in brackets. Saving your prediction runs your current construction on these inputs and checks the earlier examples too.

Build a construction in the workspace above first. This case will test those pieces.

Why does an uncertain current state require both transition rows, and which earlier information does the Markov assumption leave out?

Propagate a distribution by weighting conditional transitions, while stating which past state information the model retains.

This is a small construction problem. The lesson explains the assumptions and the parts of the full method that this example leaves out.

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