TheAlgorithms/Python · error · ValueError

gamma must be float or int

Error message

gamma must be float or int

What it means

Raised by the SupportVectorMachine constructor when gamma for the rbf kernel is not a float or int. Note that with the annotated default gamma: float = 0.0 this branch is effectively a defensive type check: only exotic objects (strings, None, numpy scalars that are not Python numbers) reach it, and Python bool passes because bool subclasses int.

Source

Thrown at machine_learning/support_vector_machines.py:69

    ValueError: gamma must be > 0
    """

    def __init__(
        self,
        *,
        regularization: float = np.inf,
        kernel: str = "linear",
        gamma: float = 0.0,
    ) -> None:
        self.regularization = regularization
        self.gamma = gamma
        if kernel == "linear":
            self.kernel = self.__linear
        elif kernel == "rbf":
            if self.gamma == 0:
                raise ValueError("rbf kernel requires gamma")
            if not isinstance(self.gamma, (float, int)):
                raise ValueError("gamma must be float or int")
            if not self.gamma > 0:
                raise ValueError("gamma must be > 0")
            self.kernel = self.__rbf
            # in the future, there could be a default value like in sklearn
            # sklear: def_gamma = 1/(n_features * X.var()) (wiki)
            # previously it was 1/(n_features)
        else:
            msg = f"Unknown kernel: {kernel}"
            raise ValueError(msg)

    # kernels
    def __linear(self, vector1: ndarray, vector2: ndarray) -> float:
        """Linear kernel (as if no kernel used at all)"""
        return np.dot(vector1, vector2)

    def __rbf(self, vector1: ndarray, vector2: ndarray) -> float:
        """
        RBF: Radial Basis Function Kernel

View on GitHub (pinned to f5988cc097)

Solutions

1. Convert the config value to float before passing: gamma=float(cfg['gamma']).
2. Do not use None or 'auto' sentinels; this API requires a concrete number.
3. Validate config at load time with a numeric check (isinstance(value, (int, float)) and not isinstance(value, bool)).

Example fix

# before
svm = SupportVectorMachine(kernel='rbf', gamma=cfg['gamma'])  # cfg value is '0.5'

# after
gamma = float(cfg['gamma'])
svm = SupportVectorMachine(kernel='rbf', gamma=gamma)

if not isinstance(gamma, (int, float)) or isinstance(gamma, bool):
    gamma = float(gamma)  # or raise your own error
svm = SupportVectorMachine(kernel='rbf', gamma=gamma)

def is_numeric_gamma(value) -> bool:
    return isinstance(value, (int, float)) and not isinstance(value, bool)

When it happens

Trigger: Passing kernel='rbf' with gamma as a string ('auto'), None, or a non-numeric object. Python True/False bypass this check since bool is an int subclass; np.float64 also passes as it registers as a float-like via isinstance in most builds or is caught here depending on version.

Common situations: Forwarding unvalidated config values (from JSON/CLI) straight into the constructor, e.g. gamma='0.5' parsed as a string; or passing gamma=None as a 'use default' sentinel, which this API does not support.

Related errors

• Unknown kernel: {kernel}
• Length of predicted and actual array must be same.
• y_true can have values -1 or 1 only.
• Test samples' feature length does not equal to that of train
• gamma value must be non-negative

AI-assisted analysis of TheAlgorithms/Python@f5988cc097 (2026-08-14). Data as JSON: /api/errors/5f97212995ed3ddc. Report an issue: GitHub.