## `eigenkit.qml.qsvr`

## `eigenkit.qml.qsvr.QSVR`

Bases: `BaseQuantumRegressor`

Quantum support vector regressor (QSVR).

It is the regression counterpart of QSVC. It uses `SVR(kernel="precomputed")` from scikit-learn on the quantum kernel matrix.

Parameters:

| Name           | Type            | Description                                                                                                                                                                                             | Default                                                                                            |
| -------------- | --------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------- |
| `feature_map`  | `str`           | The feature map. It is "Angle" (angle encoding into a product state) or "AngleCNOT" (angle encoding with ladders of controlled-NOT (CNOT) gates). Default: "Angle". It is ignored when kernel is given. | `'Angle'`                                                                                          |
| `reps`         | `int`           | The number of repetitions of the feature map. Default: 1. It is ignored when kernel is given.                                                                                                           | `1`                                                                                                |
| `C`            | `float`         | The regularisation parameter of the support vector regressor. Default: 1.0.                                                                                                                             | `1.0`                                                                                              |
| `epsilon`      | `float`         | The width of the epsilon-insensitive tube. Default: 0.1.                                                                                                                                                | `0.1`                                                                                              |
| `provider`     | `ProviderSpec`  | The backend. It takes the same options as Circuit(provider=...). Default: "numpy". It is ignored when kernel is given.                                                                                  | `'numpy'`                                                                                          |
| `random_state` | \`int           | None\`                                                                                                                                                                                                  | The seed.                                                                                          |
| `kernel`       | \`QuantumKernel | None\`                                                                                                                                                                                                  | A QuantumKernel to use instead of building one from feature_map, reps and provider. Default: None. |
| `scale`        | `float`         | The bandwidth. The inputs are multiplied by it before they reach the kernel. Default: 1.0.                                                                                                              | `1.0`                                                                                              |

Example

```python
import numpy as np
from eigenkit import QSVR

rng = np.random.default_rng(0)
X_train, X_test = rng.uniform(0, np.pi, (8, 2)), rng.uniform(0, np.pi, (3, 2))
y_train = np.sin(X_train).sum(axis=1)

model = QSVR(feature_map="Angle", reps=1)
model.fit(X_train, y_train)
predictions = model.predict(X_test)
```

### `fit(X, y)`

Fit the regressor on training data.

Parameters:

| Name | Type      | Description                                    | Default    |
| ---- | --------- | ---------------------------------------------- | ---------- |
| `X`  | `ndarray` | The samples, of shape (n_samples, n_features). | *required* |
| `y`  | `ndarray` | The float targets, of shape (n_samples,).      | *required* |

Returns:

| Type   | Description                                   |
| ------ | --------------------------------------------- |
| `QSVR` | This estimator, so that calls can be chained. |

### `load(path, *, provider=None)`

Load a fitted model from a `.eigenkit` bundle.

Load only bundles that you trust. A bundle can contain joblib pickles, and unpickling runs code, as with `torch.load` or `joblib.load`.

Parameters:

| Name       | Type           | Description | Default                                 |
| ---------- | -------------- | ----------- | --------------------------------------- |
| `path`     | \`str          | Path\`      | The path of the bundle.                 |
| `provider` | \`ProviderSpec | None\`      | A provider that replaces the saved one. |

Returns:

| Type                     | Description           |
| ------------------------ | --------------------- |
| `'BaseQuantumEstimator'` | The loaded estimator. |

Raises:

| Type        | Description                                                                                                                  |
| ----------- | ---------------------------------------------------------------------------------------------------------------------------- |
| `TypeError` | If the method is called on a subclass and the bundle holds a different class. BaseQuantumEstimator.load() accepts any class. |

### `predict(X)`

Predict continuous targets for `X`.

Parameters:

| Name | Type      | Description                                    | Default    |
| ---- | --------- | ---------------------------------------------- | ---------- |
| `X`  | `ndarray` | The samples, of shape (n_samples, n_features). | *required* |

Returns:

| Type      | Description                     |
| --------- | ------------------------------- |
| `ndarray` | One prediction for each sample. |

### `save(path)`

Save a fitted model to a `.eigenkit` bundle.

The bundles of models that hold scikit-learn objects (`QSVC`, `QSVR`, the reservoir estimators and the time-series estimators) contain joblib pickles, so loading one can run code. Share them only with people who trust you.

Parameters:

| Name   | Type  | Description | Default                                                                   |
| ------ | ----- | ----------- | ------------------------------------------------------------------------- |
| `path` | \`str | Path\`      | The path of the bundle. The suffix .eigenkit is added when it is missing. |