Experimental_Data_Analysis.py
def plot_contours_tr(clf, xx, yy, xx_tr, yy_tr, **params):
    """Plot the decision boundaries for a classifier.

    Parameters
    ----------
    ax: matplotlib axes object
    clf: a classifier
    xx: meshgrid ndarray
    yy: meshgrid ndarray
    params: dictionary of params to pass to contourf, optional
    """
    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
    Z = Z.reshape(xx.shape)
    out = plt.contourf(xx_tr, yy_tr, Z, **params)
    return out
Plot the decision boundaries for a classifier.

    Parameters
    ----------
    ax: matplotlib axes object
    clf: a classifier
    xx: meshgrid ndarray
    yy: meshgrid ndarray
    params: dictionary of params to pass to contourf, optional
    
Plot the decision boundaries for a classifier.
Parameters

matplotlib axes object
clf: a classifier
xx: meshgrid ndarray
yy: meshgrid ndarray
params: dictionary of params to pass to contourf, optional
Plot the decision boundaries for a classifier . Parameters matplotlib axes object clf : a classifier xx : meshgrid ndarray yy : meshgrid ndarray params : dictionary of params to pass to contourf optional
def plot_contours_tr(clf, xx, yy, xx_tr, yy_tr, **params):
    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
    Z = Z.reshape(xx.shape)
    out = plt.contourf(xx_tr, yy_tr, Z, **params)
    return out
def plot_contours_tr ( clf , xx , yy , xx_tr , yy_tr , ** params ) : Z = clf . predict ( np . c_ [ xx . ravel ( ) , yy . ravel ( ) ] ) Z = Z . reshape ( xx . shape ) out = plt . contourf ( xx_tr , yy_tr , Z , ** params ) return out
Plot the decision boundaries for a classifier.
Plot the decision boundaries for a classifier .
"""Plot the decision boundaries for a classifier.

    Parameters
    ----------
    ax: matplotlib axes object
    clf: a classifier
    xx: meshgrid ndarray
    yy: meshgrid ndarray
    params: dictionary of params to pass to contourf, optional
    """
clf
xx
yy
xx_tr
yy_tr