def train_plot(features, labels):
    y0, y1 = features[:, 2].min() * .9, features[:, 2].max() * 1.1
    x0, x1 = features[:, 0].min() * .9, features[:, 0].max() * 1.1
    X = np.linspace(x0, x1, 100)
    Y = np.linspace(y0, y1, 100)
    X, Y = np.meshgrid(X, Y)

    model = learn_model(1, features[:, (0, 2)], np.array(labels))
    C = apply_model(np.vstack([X.ravel(), Y.ravel()]).T,
                    model).reshape(X.shape)
    if COLOUR_FIGURE:
        cmap = ListedColormap([(1., .6, .6), (.6, 1., .6), (.6, .6, 1.)])
    else:
        cmap = ListedColormap([(1., 1., 1.), (.2, .2, .2), (.6, .6, .6)])
    plt.xlim(x0, x1)
    plt.ylim(y0, y1)
    plt.xlabel(feature_names[0])
    plt.ylabel(feature_names[2])
    plt.pcolormesh(X, Y, C, cmap=cmap)
    if COLOUR_FIGURE:
        cmap = ListedColormap([(1., .0, .0), (.0, 1., .0), (.0, .0, 1.)])
        plt.scatter(features[:, 0], features[:, 2], c=labels, cmap=cmap)
    else:
        for lab, ma in zip(range(3), "Do^"):
            plt.plot(features[labels == lab, 0],
                     features[labels == lab, 2],
                     ma,
                     c=(1., 1., 1.))
コード例 #2
0
def train_plot(features, labels):
    y0, y1 = features[:, 2].min() * .9, features[:, 2].max() * 1.1
    x0, x1 = features[:, 0].min() * .9, features[:, 0].max() * 1.1
    X = np.linspace(x0, x1, 100)
    Y = np.linspace(y0, y1, 100)
    X, Y = np.meshgrid(X, Y)

    model = learn_model(1, features[:, (0, 2)], np.array(labels))
    C = apply_model(
        np.vstack([X.ravel(), Y.ravel()]).T, model).reshape(X.shape)
    if COLOUR_FIGURE:
        cmap = ListedColormap([(1., .6, .6), (.6, 1., .6), (.6, .6, 1.)])
    else:
        cmap = ListedColormap([(1., 1., 1.), (.2, .2, .2), (.6, .6, .6)])
    plt.xlim(x0, x1)
    plt.ylim(y0, y1)
    plt.xlabel(feature_names[0])
    plt.ylabel(feature_names[2])
    plt.pcolormesh(X, Y, C, cmap=cmap)
    if COLOUR_FIGURE:
        cmap = ListedColormap([(1., .0, .0), (.0, 1., .0), (.0, .0, 1.)])
        plt.scatter(features[:, 0], features[:, 2], c=labels, cmap=cmap)
    else:
        for lab, ma in zip(range(3), "Do^"):
            plt.plot(features[labels == lab, 0], features[
                     labels == lab, 2], ma, c=(1., 1., 1.))
def train_plot(features, labels):
    y0, y1 = features[:, 2].min()*.9, features[:,2].max()*1.1
    x0, x1 = features[:, 0].min()*.9, features[:,0].max()*1.1
    X=np.linspace(x0, x1, 100)
    Y=np.linspace(y0, y1, 100)
    X, Y = np.meshgrid(X, Y)
    #meshgird is very useful to evaluate functions on a grid
    
    model = learn_model(1, features[:, (0,2)], np.array(labels))
    C = apply_model(
    np.vstack([X.ravel(), Y.ravel()]).T, model).reshape(X.shape)
    if COLOUR_FIGURE:
        cmap = ListedColormap([(1., .6, .6), (.6, 1., .6), (.6, .6, 1.)])
    else:
        cmap = ListedColormap([(1., 1, 1), (.2, .2, .2), (.6, .6, .6)])
    plt.xlim(x0,x1)
    plt.ylim(y0,y1)
    plt.xlabel(feature_names[0])
    plt.ylabel(feature_names[2])
    plt.pcolormesh(X,Y,C, cmap=cmap)
    if COLOUR_FIGURE:
        #cmap = ListedColormap([(1., .0, .0), (.0, 1, .0), (.0, .0, 1.)])
        #plt.scatter(features[:,0], features[:,2], c=labels, cmap=cmap)
        # this is a better way to plot instead of using the for .
        col=[(1., .0, .0,), (.0, 1, .0), (.0, .0, 1.)]
        for lab, c in zip(range(3), col):
            plt.scatter(features[labels==lab,0],
            features[labels==lab, 2], c=c)
    else:
        for lab, ma in zip(range(3), "Do^"):
            plt.plot(features[labels == lab, 0], features[
            labels == lab, 2], ma, c=(1.,1.,1.))
コード例 #4
0
def cross_validate(features, labels):
    error = 0.0
    for fold in range(10):
        training = np.ones(len(features), bool)
        training[fold::10] = 0
        testing = ~training
        model = learn_model(1, features[training], labels[training])
        test_error = accuracy(features[testing], labels[testing], model)
        error += test_error
    return error / 10.0
def cross_validation(features, labels):
    error = 0.0
    for fold in range(10):
        training = np.ones(len(features), bool)
        training[fold::10] = 0
        testing = ~training
        model = learn_model(1, features[training], labels[training])
        test_error = accuracy(features[testing], labels[testing], model)
        error += test_error
    return error / 10.0