Python pylab.pcolormesh() Examples

The following are 4 code examples of pylab.pcolormesh(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module pylab , or try the search function .
Example #1
Source File: helpers.py    From sklearn_pydata2015 with BSD 3-Clause "New" or "Revised" License 6 votes vote down vote up 
def plot_iris_knn():
    iris = datasets.load_iris()
    X = iris.data[:, :2]  # we only take the first two features. We could
                        # avoid this ugly slicing by using a two-dim dataset
    y = iris.target

    knn = neighbors.KNeighborsClassifier(n_neighbors=3)
    knn.fit(X, y)

    x_min, x_max = X[:, 0].min() - .1, X[:, 0].max() + .1
    y_min, y_max = X[:, 1].min() - .1, X[:, 1].max() + .1
    xx, yy = np.meshgrid(np.linspace(x_min, x_max, 100),
                         np.linspace(y_min, y_max, 100))
    Z = knn.predict(np.c_[xx.ravel(), yy.ravel()])

    # Put the result into a color plot
    Z = Z.reshape(xx.shape)
    pl.figure()
    pl.pcolormesh(xx, yy, Z, cmap=cmap_light)

    # Plot also the training points
    pl.scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold)
    pl.xlabel('sepal length (cm)')
    pl.ylabel('sepal width (cm)')
    pl.axis('tight')
Example #2
Source File: helpers.py    From MachineLearning with BSD 3-Clause "New" or "Revised" License 6 votes vote down vote up 
def plot_iris_knn():
    iris = datasets.load_iris()
    X = iris.data[:, :2]  # we only take the first two features. We could
                        # avoid this ugly slicing by using a two-dim dataset
    y = iris.target

    knn = neighbors.KNeighborsClassifier(n_neighbors=3)
    knn.fit(X, y)

    x_min, x_max = X[:, 0].min() - .1, X[:, 0].max() + .1
    y_min, y_max = X[:, 1].min() - .1, X[:, 1].max() + .1
    xx, yy = np.meshgrid(np.linspace(x_min, x_max, 100),
                         np.linspace(y_min, y_max, 100))
    Z = knn.predict(np.c_[xx.ravel(), yy.ravel()])

    # Put the result into a color plot
    Z = Z.reshape(xx.shape)
    pl.figure()
    pl.pcolormesh(xx, yy, Z, cmap=cmap_light)

    # Plot also the training points
    pl.scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold)
    pl.xlabel('sepal length (cm)')
    pl.ylabel('sepal width (cm)')
    pl.axis('tight')
Example #3
Source File: helpers.py    From ESAC-stats-2014 with BSD 2-Clause "Simplified" License 6 votes vote down vote up 
def plot_iris_knn():
    iris = datasets.load_iris()
    X = iris.data[:, :2]  # we only take the first two features. We could
                        # avoid this ugly slicing by using a two-dim dataset
    y = iris.target

    knn = neighbors.KNeighborsClassifier(n_neighbors=3)
    knn.fit(X, y)

    x_min, x_max = X[:, 0].min() - .1, X[:, 0].max() + .1
    y_min, y_max = X[:, 1].min() - .1, X[:, 1].max() + .1
    xx, yy = np.meshgrid(np.linspace(x_min, x_max, 100),
                         np.linspace(y_min, y_max, 100))
    Z = knn.predict(np.c_[xx.ravel(), yy.ravel()])

    # Put the result into a color plot
    Z = Z.reshape(xx.shape)
    pl.figure()
    pl.pcolormesh(xx, yy, Z, cmap=cmap_light)

    # Plot also the training points
    pl.scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold)
    pl.xlabel('sepal length (cm)')
    pl.ylabel('sepal width (cm)')
    pl.axis('tight')
Example #4
Source File: visu_classification.py    From JDOT with MIT License 5 votes vote down vote up 
def plot_data_classif(X,y,Z=None):
    if not Z is None:
        pl.pcolormesh(xx, yy,np.argmax(Z,2),edgecolors='face',alpha=.1, vmin=0, vmax=2)
    pl.scatter(X[:,i1],X[:,i2],c=y,edgecolors='black')#,cmap='Pastel2')