========
Examples
========
Some examples of what is possible. Please refer to the rest of the documentation for more examples.

Training and tuning an LS-SVM
=============================

.. plot::
    :include-source:

    import kerch

    tr_set, _, _, _ = kerch.dataset.factory("two_moons",   # which dataset
                                         tr_size=250)      # training size
    mdl = kerch.model.LSSVM(type="rbf",                    # kernel type
                         representation="dual")            # initiate model
    mdl.set_data_prop(data=tr_set[0],                      # data
                      labels=tr_set[1],                    # corresponding labels
                      proportions=[1, 0, 0])               # initiate dataset
    mdl.hyperopt({"gamma", "sigma"},                       # define which parameters to tune
                 max_evals=500,                            # define how many trials
                 k=10)                                     # 10-fold cross-validation
    mdl.fit()                                              # fit the optimal parameters found
    kerch.plot.plot_model(mdl)                             # plot the model using the built-in method

Out-of-sample normalized and centered kernels
=============================================

.. plot::
    :include-source:

    import kerch
    import numpy as np
    from matplotlib import pyplot as plt

    sample = np.sin(np.arange(0,15) / np.pi) + .1
    oos = np.sin(np.arange(15,30) / np.pi) + .1

    k = kerch.kernel.factory(type="polynomial", sample=sample, center=True, normalize=True)

    fig, axs = plt.subplots(2,2)

    axs[0,0].imshow(k.K, vmin=-1, vmax=1)
    axs[0,0].set_title("Sample -Sample")

    axs[0,1].imshow(k.k(y=oos), vmin=-1, vmax=1)
    axs[0,1].set_title("Sample - OOS")

    axs[1,0].imshow(k.k(x=oos), vmin=-1, vmax=1)
    axs[1,0].set_title("OOS - Sample")

    im = axs[1,1].imshow(k.k(x=oos, y=oos), vmin=-1, vmax=1)
    axs[1,1].set_title("OOS - OOS")

    for ax in axs.flat:
        ax.set_xticks([])
        ax.set_yticks([])

    fig.colorbar(im, ax=axs.ravel().tolist())