# BaseReadoutMitigator

*class *`qiskit.result.BaseReadoutMitigator`

Bases: `ABC`

Base readout error mitigator class.

## Methods

### expectation_value

*abstract *`expectation_value(data, diagonal, qubits=None, clbits=None, shots=None)`

Calculate the expectation value of a diagonal Hermitian operator.

**Parameters**

**data**(*Counts*) – Counts object to be mitigated.**diagonal**(*Callable**|**dict**|**str**|**ndarray*) – the diagonal operator. This may either be specified as a string containing I,Z,0,1 characters, or as a real valued 1D array_like object supplying the full diagonal, or as a dictionary, or as Callable.**qubits**(*Iterable**[**int**] | None*) – the physical qubits measured to obtain the counts clbits. If None these are assumed to be qubits [0, …, N-1] for N-bit counts.**clbits**(*List**[**int**] | None*) – Optional, marginalize counts to just these bits.**shots**(*int**| None*) – Optional, the total number of shots, if None shots will be calculated as the sum of all counts.

**Returns**

The mean and an upper bound of the standard deviation of operator expectation value calculated from the current counts.

**Return type**

### quasi_probabilities

*abstract *`quasi_probabilities(data, qubits=None, clbits=None, shots=None)`

Convert counts to a dictionary of quasi-probabilities

**Parameters**

**data**(*Counts*) – Counts to be mitigated.**qubits**(*Iterable**[**int**] | None*) – the physical qubits measured to obtain the counts clbits. If None these are assumed to be qubits [0, …, N-1] for N-bit counts.**clbits**(*List**[**int**] | None*) – Optional, marginalize counts to just these bits.**shots**(*int**| None*) – Optional, the total number of shots, if None shots will be calculated as the sum of all counts.

**Returns**

**A dictionary containing pairs of [output, mean] where “output”**

is the key in the dictionaries, which is the length-N bitstring of a measured standard basis state, and “mean” is the mean of non-zero quasi-probability estimates.

**Return type**