Description
Audit COO diagonal observation coverage separately from explicitly observed zero diagonal values; no assumption of implicit zeros as supplied evidence.
A sparse matrix can be stored as coordinate triplets or as pointer/index/value arrays. Stored entries are not necessarily distinct numerical nonzeros: duplicates and explicit zeros can matter to representation checks. Row and column coordinates here are zero-based, and omitted entries are handled only as stated for the particular operation.
Audit COO diagonal observation coverage separately from explicitly observed zero diagonal values; no assumption of implicit zeros as supplied evidence.
When to use this tool
Use this tool to audit COO diagonal observation coverage separately from explicitly observed zero diagonal values; no assumption of implicit zeros as supplied evidence.
How to use the tool and how it works
- Enter tripletsJson, rows, columns using the field definitions below. For JSON text, serialize the explicit records in the shown shape.
- Run the operation. Correct input errors before interpreting an output.
- Read result.missing, result.explicitZeroIndexes. Use the ordering, boundary rules and absence conditions described below.
List missing diagonal coordinate positions through min(rows, columns) and separately report original indexes of explicitly stored zero diagonal records.1
| Input field | Meaning and units | Accepted input |
|---|---|---|
| tripletsJson | JSON array of records with zero-based row, column and finite value fields. | Text, at most 8,192 UTF-16 code units; the operation’s grammar also applies. |
| rows | Declared row count; coordinate operations use 0–128, dense budgets allow larger integers. | A finite safe integer within the operation’s stated bounds. |
| columns | Declared column count; coordinate operations use 0–128, dense budgets allow larger integers. | A finite safe integer within the operation’s stated bounds. |
These are the decoded structures used in the example’s JSON text fields. Supply the corresponding serialized JSON strings as input.
{
"tripletsJson": [
{
"row": 0,
"column": 1,
"value": 2
},
{
"row": 0,
"column": 1,
"value": 3
},
{
"row": 1,
"column": 0,
"value": 0
}
]
}| Output field | Interpretation |
|---|---|
| result | Object containing the named fields below. |
| result.missing | Diagonal positions with no explicitly stored diagonal record. |
| result.explicitZeroIndexes | Original indexes of diagonal records explicitly storing zero. |
{
"input": {
"tripletsJson": "[{\"row\":0,\"column\":1,\"value\":2},{\"row\":0,\"column\":1,\"value\":3},{\"row\":1,\"column\":0,\"value\":0}]",
"rows": 2,
"columns": 2
},
"output": {
"result": {
"missing": [
0,
1
],
"explicitZeroIndexes": []
}
}
}Limitations and assumptions
- A missing diagonal record and an explicit zero are distinguished; duplicates are not combined.
- Triplets are JSON records with row, column and value. Coordinate shapes are ordinarily bounded to 128 per axis; compressed major dimensions are limited to 127 for pointer-list capacity. The dense-budget operation has separate larger shape limits and returns exact counts as decimal text.
- Every declared top-level input field is required and unexpected fields are rejected. Ordinary strings are limited to 8,192 UTF-16 code units and lists to 128 entries, with tighter operation-specific bounds where stated. JSON text has bounded nesting and node counts.
- Numeric outputs must be finite and no larger in absolute magnitude than the safe-integer limit; operations returning exact large integers use decimal text. Positions are zero-based and spans end exclusively unless the operation explicitly states a different convention.
Alternative or Complementary methods
Distinguish stored-record counts, coordinate-pattern checks and arithmetic matrix properties. Combine duplicates or prune zeros explicitly when required, and use a matrix library for numerical linear algebra.
References
Similar or alternative tools
- Sparse Coordinate Diagonal Selector
Select observed COO diagonal coordinate occurrences, preserving duplicates and zero values in rectangular matrices.
- Sparse Coordinate Axis Offset
Translate bounded COO coordinates into explicitly supplied target dimensions; out-of-target entries are rejected rather than clipped.