matten educational-path report

Input

demo: educational-path note: fixed educational demo report, not automatic expression tracing

How to read shapes first

ask what shape each input has
ask which axes align, disappear, or remain
read the output shape before reading values
convert dynamic data before numeric computation

Broadcasting

shape flow: [3, 1] + [1, 4] -> [3, 4] axis 1: left repeats across 4 columns axis 0: right repeats across 3 rows result values:

11.0 21.0 31.0 41.0
12.0 22.0 32.0 42.0
13.0 23.0 33.0 43.0

Reshape and transpose

reshape: [2, 3] -> [3, 2] reshape values:

1.0 2.0
3.0 4.0
5.0 6.0

transpose: [2, 3] -> [3, 2] transpose values:

1.0 4.0
2.0 5.0
3.0 6.0

meaning: reshape changes grouping; transpose changes coordinate meaning

Axis reductions

mean_axis(0): [2, 3] -> [3] mean_axis(0) keeps columns: [2.5, 3.5, 4.5] mean_axis(1): [2, 3] -> [2] mean_axis(1) keeps rows: [2.0, 5.0]

Matrix multiplication

shape flow: [2, 3] @ [3, 4] -> [2, 4] shared inner dimension: 3 result values:

38.0 44.0  50.0  56.0
83.0 98.0 113.0 128.0

Dynamic readiness

dynamic shape: [2, 3] none mask:

0.0 0.0 1.0
0.0 0.0 0.0

numeric mask: strict policy readiness

1.0 0.0 0.0
1.0 0.0 1.0

Text values are not numeric-ready under the strict mask next step: clean values, then call try_numeric()

operation: standardize_columns(input) shape flow: [3, 2] -> [3, 2] before column mean: [10.000, 100.000] before column population std: [1.633, 16.330] after column mean: [0.000, 0.000] after column population std: [1.000, 1.000]

What this report is not

not a public API
not source scanning
not a renderer
not model-quality analysis

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