d2l.plt.scatter()是什么函数

d2l.plt.scatter() 是 Deep Learning - The Straight Dope (D2L) 书中定义的一个函数，用于绘制散点图。它是基于 Matplotlib 库实现的，具有以下特点：

- 支持自定义点的颜色、大小、形状等属性。
- 支持添加标题、坐标轴标签等注释。
- 支持同时绘制多个散点图。

该函数的定义如下：

def set_figsize(figsize=(3.5, 2.5)):
    """Set matplotlib figure size."""
    use_svg_display()
    d2l.plt.rcParams['figure.figsize'] = figsize

def use_svg_display():
    """Use svg format to display plot in jupyter."""
    d2l.plt.rcParams['svg.fonttype'] = 'none'

def plot(X, Y=None, xlabel=None, ylabel=None, legend=None, xlim=None,
         ylim=None, xscale='linear', yscale='linear', fmts=None,
         figsize=(3.5, 2.5), axes=None):
    """Plot multiple lines."""
    if legend is None:
        legend = []
    set_figsize(figsize)
    axes = axes if axes else d2l.plt.gca()
    # Return True if X (tensor or list) has more than 1 dimension
    def has_one_dim(X):
        if isinstance(X, np.ndarray) or isinstance(X, list):
            return len(X.shape) == 1
        else:
            return len(X.size()) == 1
    # Convert Y to list so that len(Y) is always defined
    if Y is None:
        Y = []
    if not isinstance(Y, list):
        Y = [Y]
    if not isinstance(X, list):
        X = [X] * len(Y)
    if len(X) != len(Y):
        X = X * len(Y)
    # Format lines and points
    if fmts is None:
        fmts = ['-'] * len(X)
    elif isinstance(fmts, str):
        fmts = [fmts] * len(X)
    # Plot
    for x, y, fmt in zip(X, Y, fmts):
        if has_one_dim(x):
            x = np.arange(len(y)) + 1
        axes.plot(x, y, fmt)
    axes.set_xlabel(xlabel)
    axes.set_ylabel(ylabel)
    axes.set_xscale(xscale)
    axes.set_yscale(yscale)
    if xlim:
        axes.set_xlim(xlim)
    if ylim:
        axes.set_ylim(ylim)
    if legend:
        axes.legend(legend)
    axes.grid()
    d2l.plt.show()

def show_images(imgs, num_rows, num_cols, scale=2):
    """Plot a list of images."""
    figsize = (num_cols * scale, num_rows * scale)
    _, axes = d2l.plt.subplots(num_rows, num_cols, figsize=figsize)
    for i in range(num_rows):
        for j in range(num_cols):
            axes[i][j].imshow(imgs[i * num_cols + j])
            axes[i][j].axes.get_xaxis().set_visible(False)
            axes[i][j].axes.get_yaxis().set_visible(False)
    return axes

def plot_learning_curves(train_acc, test_acc, train_loss, test_loss):
    """Plot the learning curves."""
    d2l.plt.plot(train_acc, linestyle='-', color='blue', label='train accuracy')
    d2l.plt.plot(test_acc, linestyle='--', color='red', label='test accuracy')
    d2l.plt.xlabel('epochs')
    d2l.plt.ylabel('accuracy')
    d2l.plt.legend(loc='upper right')
    d2l.plt.twinx()
    d2l.plt.plot(train_loss, linestyle='-', color='green', label='train loss')
    d2l.plt.plot(test_loss, linestyle='--', color='orange', label='test loss')
    d2l.plt.ylabel('loss')
    d2l.plt.legend(loc='upper left')
    d2l.plt.show()

def use_svg_display():
    """Use svg format to display plot in jupyter."""
    d2l.plt.rcParams['svg.fonttype'] = 'none'

def set_figsize(figsize=(3.5, 2.5)):
    """Set matplotlib figure size."""
    use_svg_display()
    d2l.plt.rcParams['figure.figsize'] = figsize


def plot(X, Y=None, xlabel=None, ylabel=None, legend=None, xlim=None,
         ylim=None, xscale='linear', yscale='linear', fmts=None,
         figsize=(3.5, 2.5), axes=None):
    """Plot multiple lines."""
    if legend is None:
        legend = []
    set_figsize(figsize)
    axes = axes if axes else d2l.plt.gca()
    # Return True if X (tensor or list) has more than 1 dimension
    def has_one_dim(X):
        if isinstance(X, np.ndarray) or isinstance(X, list):
            return len(X.shape) == 1
        else:
            return len(X.size()) == 1
    # Convert Y to list so that len(Y) is always defined
    if Y is None:
        Y = []
    if not isinstance(Y, list):
        Y = [Y]
    if not isinstance(X, list):
        X = [X] * len(Y)
    if len(X) != len(Y):
        X = X * len(Y)
    # Format lines and points
    if fmts is None:
        fmts = ['-'] * len(X)
    elif isinstance(fmts, str):
        fmts = [fmts] * len(X)
    # Plot
    for x, y, fmt in zip(X, Y, fmts):
        if has_one_dim(x):
            x = np.arange(len(y)) + 1
        axes.plot(x, y, fmt)
    axes.set_xlabel(xlabel)
    axes.set_ylabel(ylabel)
    axes.set_xscale(xscale)
    axes.set_yscale(yscale)
    if xlim:
        axes.set_xlim(xlim)
    if ylim:
        axes.set_ylim(ylim)
    if legend:
        axes.legend(legend)
    axes.grid()
    d2l.plt.show()

def show_images(imgs, num_rows, num_cols, scale=2):
    """Plot a list of images."""
    figsize = (num_cols * scale, num_rows * scale)
    _, axes = d2l.plt.subplots(num_rows, num_cols, figsize=figsize)
    for i in range(num_rows):
        for j in range(num_cols):
            axes[i][j].imshow(imgs[i * num_cols + j])
            axes[i][j].axes.get_xaxis().set_visible(False)
            axes[i][j].axes.get_yaxis().set_visible(False)
    return axes

def plot_learning_curves(train_acc, test_acc, train_loss, test_loss):
    """Plot the learning curves."""
    d2l.plt.plot(train_acc, linestyle='-', color='blue', label='train accuracy')
    d2l.plt.plot(test_acc, linestyle='--', color='red', label='test accuracy')
    d2l.plt.xlabel('epochs')
    d2l.plt.ylabel('accuracy')
    d2l.plt.legend(loc='upper right')
    d2l.plt.twinx()
    d2l.plt.plot(train_loss, linestyle='-', color='green', label='train loss')
    d2l.plt.plot(test_loss, linestyle='--', color='orange', label='test loss')
    d2l.plt.ylabel('loss')
    d2l.plt.legend(loc='upper left')
    d2l.plt.show()

def set_axes(axes, xlabel, ylabel, xlim, ylim, xscale, yscale, legend):
    """Set the axes for matplotlib."""
    axes.set_xlabel(xlabel)
    axes.set_ylabel(ylabel)
    axes.set_xscale(xscale)
    axes.set_yscale(yscale)
    if xlim:
        axes.set_xlim(xlim)
    if ylim:
        axes.set_ylim(ylim)
    if legend:
        axes.legend(legend)
    axes.grid()