TheAlgorithms/Python · error · ValueError

Wrong input data's shape... dataset : {dataset.shape[1]}, va

Error message

Wrong input data's shape... dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}

What it means

Raised by similarity_search when dataset and value_array agree on dimension count but their feature (column) dimension shape[1] differs. Each query vector must have exactly as many coordinates as the dataset points so that pairwise Euclidean distances are defined.

Source

Thrown at machine_learning/similarity_search.py:113

        ...
    TypeError: Input data have different datatype...
    dataset : float32, value_array : int32
    """

    if dataset.ndim != value_array.ndim:
        msg = (
            "Wrong input data's dimensions... "
            f"dataset : {dataset.ndim}, value_array : {value_array.ndim}"
        )
        raise ValueError(msg)

    try:
        if dataset.shape[1] != value_array.shape[1]:
            msg = (
                "Wrong input data's shape... "
                f"dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}"
            )
            raise ValueError(msg)
    except IndexError:
        if dataset.ndim != value_array.ndim:
            raise TypeError("Wrong shape")

    if dataset.dtype != value_array.dtype:
        msg = (
            "Input data have different datatype... "
            f"dataset : {dataset.dtype}, value_array : {value_array.dtype}"
        )
        raise TypeError(msg)

    answer = []

    for value in value_array:
        dist = euclidean(value, dataset[0])
        vector = dataset[0].tolist()

        for dataset_value in dataset[1:]:

View on GitHub (pinned to f5988cc097)

Solutions

1. Align feature counts: recompute query vectors from the same feature extraction used to build the dataset.
2. Drop or add the offending column explicitly (e.g. remove the ID column) so both shape[1] values match.
3. Assert equality of shapes before the call: dataset.shape[1] == value_array.shape[1].

Example fix

# before
result = similarity_search(dataset, queries)  # (100, 3) vs (10, 2)

# after
assert dataset.shape[1] == queries.shape[1]
result = similarity_search(dataset, queries)

assert dataset.shape[1] == value_array.shape[1], (
    f"feature mismatch: {dataset.shape[1]} vs {value_array.shape[1]}"
)
result = similarity_search(dataset, value_array)

When it happens

Trigger: Calling similarity_search(dataset, value_array) where dataset.shape[1] != value_array.shape[1], e.g. dataset points of length 3 against query vectors of length 2. With equal ndim the IndexError guard is skipped and the explicit ValueError fires.

Common situations: Querying with vectors built from a different feature pipeline than the reference dataset, dropping a column during preprocessing of one side only, or loading datasets with an trailing index column on one array.

Related errors

• Wrong input data's dimensions... dataset : {dataset.ndim}, v
• Input data have different datatype... dataset : {dataset.dty
• Test samples' feature length does not equal to that of train
• Expected a_coeffs to have {self.order + 1} elements for {sel
• Expected b_coeffs to have {self.order + 1} elements for {sel

AI-assisted analysis of TheAlgorithms/Python@f5988cc097 (2026-08-14). Data as JSON: /api/errors/62ce6b2662b665cc. Report an issue: GitHub.