python类grid()的实例源码

def plot_bias_variance(data_sizes, train_errors, test_errors, name, title):
    pylab.figure(num=None, figsize=(6, 5))
    pylab.ylim([0.0, 1.0])
    pylab.xlabel('Data set size')
    pylab.ylabel('Error')
    pylab.title("Bias-Variance for '%s'" % name)
    pylab.plot(
        data_sizes, test_errors, "--", data_sizes, train_errors, "b-", lw=1)
    pylab.legend(["train error", "test error"], loc="upper right")
    pylab.grid(True, linestyle='-', color='0.75')
    pylab.savefig(os.path.join(CHART_DIR, "bv_" + name.replace(" ", "_") + ".png"), bbox_inches="tight")

def plot_k_complexity(ks, train_errors, test_errors):
    pylab.figure(num=None, figsize=(6, 5))
    pylab.ylim([0.0, 1.0])
    pylab.xlabel('k')
    pylab.ylabel('Error')
    pylab.title('Errors for for different values of k')
    pylab.plot(
        ks, test_errors, "--", ks, train_errors, "-", lw=1)
    pylab.legend(["train error", "test error"], loc="upper right")
    pylab.grid(True, linestyle='-', color='0.75')
    pylab.savefig(os.path.join(CHART_DIR, "kcomplexity.png"), bbox_inches="tight")

def plot_pr(auc_score, name, precision, recall, label=None):
    pylab.figure(num=None, figsize=(6, 5))
    pylab.xlim([0.0, 1.0])
    pylab.ylim([0.0, 1.0])
    pylab.xlabel('Recall')
    pylab.ylabel('Precision')
    pylab.title('P/R (AUC=%0.2f) / %s' % (auc_score, label))
    pylab.fill_between(recall, precision, alpha=0.5)
    pylab.grid(True, linestyle='-', color='0.75')
    pylab.plot(recall, precision, lw=1)
    filename = name.replace(" ", "_")
    pylab.savefig(os.path.join(CHART_DIR, "pr_" + filename + ".png"))

def plot_feat_hist(data_name_list, filename=None):
    if len(data_name_list)>1:
        assert filename is not None

    pylab.figure(num=None, figsize=(8, 6))
    num_rows = 1 + (len(data_name_list) - 1) / 2
    num_cols = 1 if len(data_name_list) == 1 else 2
    pylab.figure(figsize=(5 * num_cols, 4 * num_rows))

    for i in range(num_rows):
        for j in range(num_cols):
            pylab.subplot(num_rows, num_cols, 1 + i * num_cols + j)
            x, name = data_name_list[i * num_cols + j]
            pylab.title(name)
            pylab.xlabel('Value')
            pylab.ylabel('Fraction')
            # the histogram of the data
            max_val = np.max(x)
            if max_val <= 1.0:
                bins = 50
            elif max_val > 50:
                bins = 50
            else:
                bins = max_val
            n, bins, patches = pylab.hist(
                x,  normed=1, facecolor='blue', alpha=0.75)

            pylab.grid(True)

    if not filename:
        filename = "feat_hist_%s.png" % name.replace(" ", "_")

    pylab.savefig(os.path.join(CHART_DIR, filename), bbox_inches="tight")

def plot_bias_variance(data_sizes, train_errors, test_errors, name, title):
    pylab.figure(num=None, figsize=(6, 5))
    pylab.ylim([0.0, 1.0])
    pylab.xlabel('Data set size')
    pylab.ylabel('Error')
    pylab.title("Bias-Variance for '%s'" % name)
    pylab.plot(
        data_sizes, test_errors, "--", data_sizes, train_errors, "b-", lw=1)
    pylab.legend(["train error", "test error"], loc="upper right")
    pylab.grid(True, linestyle='-', color='0.75')
    pylab.savefig(os.path.join(CHART_DIR, "bv_" + name.replace(" ", "_") + ".png"), bbox_inches="tight")

def plot_k_complexity(ks, train_errors, test_errors):
    pylab.figure(num=None, figsize=(6, 5))
    pylab.ylim([0.0, 1.0])
    pylab.xlabel('k')
    pylab.ylabel('Error')
    pylab.title('Errors for for different values of k')
    pylab.plot(
        ks, test_errors, "--", ks, train_errors, "-", lw=1)
    pylab.legend(["train error", "test error"], loc="upper right")
    pylab.grid(True, linestyle='-', color='0.75')
    pylab.savefig(os.path.join(CHART_DIR, "kcomplexity.png"), bbox_inches="tight")

def plot_fig(values, title, x_str, y_str, path, best_iter, std_vals=None):
    """Plot some values.
    Input:
         values: list or numpy.ndarray of values to plot (y)
         title: string; the title of the plot.
         x_str: string; the name of the x axis.
         y_str: string; the name of the y axis.
         path: string; path where to save the figure.
         best_iter: integer. The epoch of the best iteration.
         std_val: List or numpy.ndarray of standad deviation values that
             corresponds to each value in 'values'.
    """
    floating = 6
    prec = "%." + str(floating) + "f"

    if best_iter >= 0:
        if isinstance(values, list):
            if best_iter >= len(values):
                best_iter = -1
        if isinstance(values, np.ndarray):
            if best_iter >= np.size:
                best_iter = -1

        v = str(prec % np.float(values[best_iter]))
    else:
        v = str(prec % np.float(values[-1]))
        best_iter = -1
    if best_iter == -1:
        best_iter = len(values)
    fig = plt.figure()
    plt.plot(
        values,
        label="lower val: " + v + " at " + str(best_iter) + " " +
        x_str)
    plt.xlabel(x_str)
    plt.ylabel(y_str)
    plt.title(title, fontsize=8)
    plt.legend(loc='upper right', fancybox=True, shadow=True, prop={'size': 8})
    plt.grid(True)
    fig.savefig(path, bbox_inches='tight')
    plt.close('all')
    del fig

def plot(self, *args, **kwargs):
        """
        Plot samples from the frequency distribution
        displaying the most frequent sample first.  If an integer
        parameter is supplied, stop after this many samples have been
        plotted.  For a cumulative plot, specify cumulative=True.
        (Requires Matplotlib to be installed.)

        :param title: The title for the graph
        :type title: str
        :param cumulative: A flag to specify whether the plot is cumulative (default = False)
        :type title: bool
        """
        try:
            from matplotlib import pylab
        except ImportError:
            raise ValueError('The plot function requires matplotlib to be installed.'
                         'See http://matplotlib.org/')

        if len(args) == 0:
            args = [len(self)]
        samples = [item for item, _ in self.most_common(*args)]

        cumulative = _get_kwarg(kwargs, 'cumulative', False)
        if cumulative:
            freqs = list(self._cumulative_frequencies(samples))
            ylabel = "Cumulative Counts"
        else:
            freqs = [self[sample] for sample in samples]
            ylabel = "Counts"
        # percents = [f * 100 for f in freqs]  only in ProbDist?

        pylab.grid(True, color="silver")
        if not "linewidth" in kwargs:
            kwargs["linewidth"] = 2
        if "title" in kwargs:
            pylab.title(kwargs["title"])
            del kwargs["title"]
        pylab.plot(freqs, **kwargs)
        pylab.xticks(range(len(samples)), [compat.text_type(s) for s in samples], rotation=90)
        pylab.xlabel("Samples")
        pylab.ylabel(ylabel)
        pylab.show()

def plot(self, *args, **kwargs):
        """
        Plot the given samples from the conditional frequency distribution.
        For a cumulative plot, specify cumulative=True.
        (Requires Matplotlib to be installed.)

        :param samples: The samples to plot
        :type samples: list
        :param title: The title for the graph
        :type title: str
        :param conditions: The conditions to plot (default is all)
        :type conditions: list
        """
        try:
            from matplotlib import pylab
        except ImportError:
            raise ValueError('The plot function requires matplotlib to be installed.'
                         'See http://matplotlib.org/')

        cumulative = _get_kwarg(kwargs, 'cumulative', False)
        conditions = _get_kwarg(kwargs, 'conditions', sorted(self.conditions()))
        title = _get_kwarg(kwargs, 'title', '')
        samples = _get_kwarg(kwargs, 'samples',
                             sorted(set(v for c in conditions for v in self[c])))  # this computation could be wasted
        if not "linewidth" in kwargs:
            kwargs["linewidth"] = 2

        for condition in conditions:
            if cumulative:
                freqs = list(self[condition]._cumulative_frequencies(samples))
                ylabel = "Cumulative Counts"
                legend_loc = 'lower right'
            else:
                freqs = [self[condition][sample] for sample in samples]
                ylabel = "Counts"
                legend_loc = 'upper right'
            # percents = [f * 100 for f in freqs] only in ConditionalProbDist?
            kwargs['label'] = "%s" % condition
            pylab.plot(freqs, *args, **kwargs)

        pylab.legend(loc=legend_loc)
        pylab.grid(True, color="silver")
        pylab.xticks(range(len(samples)), [compat.text_type(s) for s in samples], rotation=90)
        if title:
            pylab.title(title)
        pylab.xlabel("Samples")
        pylab.ylabel(ylabel)
        pylab.show()

def plot_mi_demo():
    np.random.seed(0)  # to reproduce the data later on
    pylab.clf()
    pylab.figure(num=None, figsize=(8, 8))

    x = np.arange(0, 10, 0.2)

    pylab.subplot(221)
    y = 0.5 * x + norm.rvs(1, scale=.01, size=len(x))
    _plot_mi_func(x, y)

    pylab.subplot(222)
    y = 0.5 * x + norm.rvs(1, scale=.1, size=len(x))
    _plot_mi_func(x, y)

    pylab.subplot(223)
    y = 0.5 * x + norm.rvs(1, scale=1, size=len(x))
    _plot_mi_func(x, y)

    pylab.subplot(224)
    y = norm.rvs(1, scale=10, size=len(x))
    _plot_mi_func(x, y)

    pylab.autoscale(tight=True)
    pylab.grid(True)

    filename = "mi_demo_1.png"
    pylab.savefig(os.path.join(CHART_DIR, filename), bbox_inches="tight")

    pylab.clf()
    pylab.figure(num=None, figsize=(8, 8))

    x = np.arange(-5, 5, 0.2)

    pylab.subplot(221)
    y = 0.5 * x ** 2 + norm.rvs(1, scale=.01, size=len(x))
    _plot_mi_func(x, y)

    pylab.subplot(222)
    y = 0.5 * x ** 2 + norm.rvs(1, scale=.1, size=len(x))
    _plot_mi_func(x, y)

    pylab.subplot(223)
    y = 0.5 * x ** 2 + norm.rvs(1, scale=1, size=len(x))
    _plot_mi_func(x, y)

    pylab.subplot(224)
    y = 0.5 * x ** 2 + norm.rvs(1, scale=10, size=len(x))
    _plot_mi_func(x, y)

    pylab.autoscale(tight=True)
    pylab.grid(True)

    filename = "mi_demo_2.png"
    pylab.savefig(os.path.join(CHART_DIR, filename), bbox_inches="tight")

def plot_correlation_demo():
    np.random.seed(0)  # to reproduce the data later on
    pylab.clf()
    pylab.figure(num=None, figsize=(8, 8))

    x = np.arange(0, 10, 0.2)

    pylab.subplot(221)
    y = 0.5 * x + norm.rvs(1, scale=.01, size=len(x))
    _plot_correlation_func(x, y)

    pylab.subplot(222)
    y = 0.5 * x + norm.rvs(1, scale=.1, size=len(x))
    _plot_correlation_func(x, y)

    pylab.subplot(223)
    y = 0.5 * x + norm.rvs(1, scale=1, size=len(x))
    _plot_correlation_func(x, y)

    pylab.subplot(224)
    y = norm.rvs(1, scale=10, size=len(x))
    _plot_correlation_func(x, y)

    pylab.autoscale(tight=True)
    pylab.grid(True)

    filename = "corr_demo_1.png"
    pylab.savefig(os.path.join(CHART_DIR, filename), bbox_inches="tight")

    pylab.clf()
    pylab.figure(num=None, figsize=(8, 8))

    x = np.arange(-5, 5, 0.2)

    pylab.subplot(221)
    y = 0.5 * x ** 2 + norm.rvs(1, scale=.01, size=len(x))
    _plot_correlation_func(x, y)

    pylab.subplot(222)
    y = 0.5 * x ** 2 + norm.rvs(1, scale=.1, size=len(x))
    _plot_correlation_func(x, y)

    pylab.subplot(223)
    y = 0.5 * x ** 2 + norm.rvs(1, scale=1, size=len(x))
    _plot_correlation_func(x, y)

    pylab.subplot(224)
    y = 0.5 * x ** 2 + norm.rvs(1, scale=10, size=len(x))
    _plot_correlation_func(x, y)

    pylab.autoscale(tight=True)
    pylab.grid(True)

    filename = "corr_demo_2.png"
    pylab.savefig(os.path.join(CHART_DIR, filename), bbox_inches="tight")

def plot_simple_demo_1():
    pylab.clf()
    fig = pylab.figure(num=None, figsize=(10, 4))
    pylab.subplot(121)

    title = "Original feature space"
    pylab.title(title)
    pylab.xlabel("$X_1$")
    pylab.ylabel("$X_2$")

    x1 = np.arange(0, 10, .2)
    x2 = x1 + np.random.normal(scale=1, size=len(x1))

    good = (x1 > 5) | (x2 > 5)
    bad = ~good

    x1g = x1[good]
    x2g = x2[good]
    pylab.scatter(x1g, x2g, edgecolor="blue", facecolor="blue")

    x1b = x1[bad]
    x2b = x2[bad]
    pylab.scatter(x1b, x2b, edgecolor="red", facecolor="white")

    pylab.grid(True)

    pylab.subplot(122)

    X = np.c_[(x1, x2)]

    pca = decomposition.PCA(n_components=1)
    Xtrans = pca.fit_transform(X)

    Xg = Xtrans[good]
    Xb = Xtrans[bad]

    pylab.scatter(
        Xg[:, 0], np.zeros(len(Xg)), edgecolor="blue", facecolor="blue")
    pylab.scatter(
        Xb[:, 0], np.zeros(len(Xb)), edgecolor="red", facecolor="white")
    title = "Transformed feature space"
    pylab.title(title)
    pylab.xlabel("$X'$")
    fig.axes[1].get_yaxis().set_visible(False)

    print(pca.explained_variance_ratio_)

    pylab.grid(True)

    pylab.autoscale(tight=True)
    filename = "pca_demo_1.png"
    pylab.savefig(os.path.join(CHART_DIR, filename), bbox_inches="tight")

def plot_simple_demo_2():
    pylab.clf()
    fig = pylab.figure(num=None, figsize=(10, 4))
    pylab.subplot(121)

    title = "Original feature space"
    pylab.title(title)
    pylab.xlabel("$X_1$")
    pylab.ylabel("$X_2$")

    x1 = np.arange(0, 10, .2)
    x2 = x1 + np.random.normal(scale=1, size=len(x1))

    good = x1 > x2
    bad = ~good

    x1g = x1[good]
    x2g = x2[good]
    pylab.scatter(x1g, x2g, edgecolor="blue", facecolor="blue")

    x1b = x1[bad]
    x2b = x2[bad]
    pylab.scatter(x1b, x2b, edgecolor="red", facecolor="white")

    pylab.grid(True)

    pylab.subplot(122)

    X = np.c_[(x1, x2)]

    pca = decomposition.PCA(n_components=1)
    Xtrans = pca.fit_transform(X)

    Xg = Xtrans[good]
    Xb = Xtrans[bad]

    pylab.scatter(
        Xg[:, 0], np.zeros(len(Xg)), edgecolor="blue", facecolor="blue")
    pylab.scatter(
        Xb[:, 0], np.zeros(len(Xb)), edgecolor="red", facecolor="white")
    title = "Transformed feature space"
    pylab.title(title)
    pylab.xlabel("$X'$")
    fig.axes[1].get_yaxis().set_visible(False)

    print(pca.explained_variance_ratio_)

    pylab.grid(True)

    pylab.autoscale(tight=True)
    filename = "pca_demo_2.png"
    pylab.savefig(os.path.join(CHART_DIR, filename), bbox_inches="tight")

def plotScatter(self, xList, yList, saveFigPath):
        '''
        ?????? xList ???? yList ????????????
        ?????? saveFigPath ????
        :param xList: ????
        :param yList: ????
        :param saveFigPath: ????????
        :return:
        '''
        # ????????????
        # ??????????? 2
        # ???????? 2 ?????
        if len(xList[0]) >= 2:
            x1List = map(lambda x: x[0], xList)
            x2List = map(lambda x: x[1], xList)
        else:
            # 1 ? 2 ???????? 2 ?
            x1List = x2List = map(lambda x: x[0], xList)
        # ????
        scatterFig= plt.figure(saveFigPath)
        # ?????????
        colorDict = {-1: 'm', 1: 'r', 2: 'b', 3: 'pink', 4: 'orange'}
        # ?????
        map(lambda idx: \
                plt.scatter(x1List[idx], \
                            x2List[idx], \
                            marker='o', \
                            color=colorDict[yList[idx]], \
                            label=yList[idx]), \
            xrange(len(x1List)))
        # ?????????
        # ySet = set(yList)
        # map(lambda y: \
        #         plt.legend(str(y), \
        #                    loc='best'), \
        #     ySet)

        # ??????????????
        plt.title(saveFigPath)
        plt.xlabel(r'$x^1$')
        plt.ylabel(r'$x^2$')
        plt.grid(True)
        plt.savefig(saveFigPath)
        plt.show()

def plot(self, *args, **kwargs):
        """
        Plot samples from the frequency distribution
        displaying the most frequent sample first.  If an integer
        parameter is supplied, stop after this many samples have been
        plotted.  For a cumulative plot, specify cumulative=True.
        (Requires Matplotlib to be installed.)

        :param title: The title for the graph
        :type title: str
        :param cumulative: A flag to specify whether the plot is cumulative (default = False)
        :type title: bool
        """
        try:
            from matplotlib import pylab
        except ImportError:
            raise ValueError('The plot function requires matplotlib to be installed.'
                         'See http://matplotlib.org/')

        if len(args) == 0:
            args = [len(self)]
        samples = [item for item, _ in self.most_common(*args)]

        cumulative = _get_kwarg(kwargs, 'cumulative', False)
        if cumulative:
            freqs = list(self._cumulative_frequencies(samples))
            ylabel = "Cumulative Counts"
        else:
            freqs = [self[sample] for sample in samples]
            ylabel = "Counts"
        # percents = [f * 100 for f in freqs]  only in ProbDist?

        pylab.grid(True, color="silver")
        if not "linewidth" in kwargs:
            kwargs["linewidth"] = 2
        if "title" in kwargs:
            pylab.title(kwargs["title"])
            del kwargs["title"]
        pylab.plot(freqs, **kwargs)
        pylab.xticks(range(len(samples)), [compat.text_type(s) for s in samples], rotation=90)
        pylab.xlabel("Samples")
        pylab.ylabel(ylabel)
        pylab.show()

def plot(self, *args, **kwargs):
        """
        Plot the given samples from the conditional frequency distribution.
        For a cumulative plot, specify cumulative=True.
        (Requires Matplotlib to be installed.)

        :param samples: The samples to plot
        :type samples: list
        :param title: The title for the graph
        :type title: str
        :param conditions: The conditions to plot (default is all)
        :type conditions: list
        """
        try:
            from matplotlib import pylab
        except ImportError:
            raise ValueError('The plot function requires matplotlib to be installed.'
                         'See http://matplotlib.org/')

        cumulative = _get_kwarg(kwargs, 'cumulative', False)
        conditions = _get_kwarg(kwargs, 'conditions', sorted(self.conditions()))
        title = _get_kwarg(kwargs, 'title', '')
        samples = _get_kwarg(kwargs, 'samples',
                             sorted(set(v for c in conditions for v in self[c])))  # this computation could be wasted
        if not "linewidth" in kwargs:
            kwargs["linewidth"] = 2

        for condition in conditions:
            if cumulative:
                freqs = list(self[condition]._cumulative_frequencies(samples))
                ylabel = "Cumulative Counts"
                legend_loc = 'lower right'
            else:
                freqs = [self[condition][sample] for sample in samples]
                ylabel = "Counts"
                legend_loc = 'upper right'
            # percents = [f * 100 for f in freqs] only in ConditionalProbDist?
            kwargs['label'] = "%s" % condition
            pylab.plot(freqs, *args, **kwargs)

        pylab.legend(loc=legend_loc)
        pylab.grid(True, color="silver")
        pylab.xticks(range(len(samples)), [compat.text_type(s) for s in samples], rotation=90)
        if title:
            pylab.title(title)
        pylab.xlabel("Samples")
        pylab.ylabel(ylabel)
        pylab.show()

def plot(self, *args, **kwargs):
        """
        Plot samples from the frequency distribution
        displaying the most frequent sample first.  If an integer
        parameter is supplied, stop after this many samples have been
        plotted.  For a cumulative plot, specify cumulative=True.
        (Requires Matplotlib to be installed.)

        :param title: The title for the graph
        :type title: str
        :param cumulative: A flag to specify whether the plot is cumulative (default = False)
        :type title: bool
        """
        try:
            from matplotlib import pylab
        except ImportError:
            raise ValueError('The plot function requires matplotlib to be installed.'
                         'See http://matplotlib.org/')

        if len(args) == 0:
            args = [len(self)]
        samples = [item for item, _ in self.most_common(*args)]

        cumulative = _get_kwarg(kwargs, 'cumulative', False)
        if cumulative:
            freqs = list(self._cumulative_frequencies(samples))
            ylabel = "Cumulative Counts"
        else:
            freqs = [self[sample] for sample in samples]
            ylabel = "Counts"
        # percents = [f * 100 for f in freqs]  only in ProbDist?

        pylab.grid(True, color="silver")
        if not "linewidth" in kwargs:
            kwargs["linewidth"] = 2
        if "title" in kwargs:
            pylab.title(kwargs["title"])
            del kwargs["title"]
        pylab.plot(freqs, **kwargs)
        pylab.xticks(range(len(samples)), [compat.text_type(s) for s in samples], rotation=90)
        pylab.xlabel("Samples")
        pylab.ylabel(ylabel)
        pylab.show()

def plot(self, *args, **kwargs):
        """
        Plot the given samples from the conditional frequency distribution.
        For a cumulative plot, specify cumulative=True.
        (Requires Matplotlib to be installed.)

        :param samples: The samples to plot
        :type samples: list
        :param title: The title for the graph
        :type title: str
        :param conditions: The conditions to plot (default is all)
        :type conditions: list
        """
        try:
            from matplotlib import pylab
        except ImportError:
            raise ValueError('The plot function requires matplotlib to be installed.'
                         'See http://matplotlib.org/')

        cumulative = _get_kwarg(kwargs, 'cumulative', False)
        conditions = _get_kwarg(kwargs, 'conditions', sorted(self.conditions()))
        title = _get_kwarg(kwargs, 'title', '')
        samples = _get_kwarg(kwargs, 'samples',
                             sorted(set(v for c in conditions for v in self[c])))  # this computation could be wasted
        if not "linewidth" in kwargs:
            kwargs["linewidth"] = 2

        for condition in conditions:
            if cumulative:
                freqs = list(self[condition]._cumulative_frequencies(samples))
                ylabel = "Cumulative Counts"
                legend_loc = 'lower right'
            else:
                freqs = [self[condition][sample] for sample in samples]
                ylabel = "Counts"
                legend_loc = 'upper right'
            # percents = [f * 100 for f in freqs] only in ConditionalProbDist?
            kwargs['label'] = "%s" % condition
            pylab.plot(freqs, *args, **kwargs)

        pylab.legend(loc=legend_loc)
        pylab.grid(True, color="silver")
        pylab.xticks(range(len(samples)), [compat.text_type(s) for s in samples], rotation=90)
        if title:
            pylab.title(title)
        pylab.xlabel("Samples")
        pylab.ylabel(ylabel)
        pylab.show()

def plot_fig(values, title, x_str, y_str, path, best_iter, std_vals=None):
    """Plot some values.
    Input:
         values: list or numpy.ndarray of values to plot (y)
         title: string; the title of the plot.
         x_str: string; the name of the x axis.
         y_str: string; the name of the y axis.
         path: string; path where to save the figure.
         best_iter: integer. The epoch of the best iteration.
         std_val: List or numpy.ndarray of standad deviation values that
             corresponds to each value in 'values'.
    """
    floating = 6
    prec = "%." + str(floating) + "f"

    if best_iter >= 0:
        if isinstance(values, list):
            if best_iter >= len(values):
                best_iter = -1
        if isinstance(values, np.ndarray):
            if best_iter >= np.size:
                best_iter = -1

        v = str(prec % np.float(values[best_iter]))
    else:
        v = str(prec % np.float(values[-1]))
        best_iter = -1
    if best_iter == -1:
        best_iter = len(values)
    fig = plt.figure()
    plt.plot(
        values,
        label="lower val: " + v + " at " + str(best_iter) + " " +
        x_str)
    plt.xlabel(x_str)
    plt.ylabel(y_str)
    plt.title(title, fontsize=8)
    plt.legend(loc='upper right', fancybox=True, shadow=True, prop={'size': 8})
    plt.grid(True)
    fig.savefig(path, bbox_inches='tight')
    plt.close('all')
    del fig

def plot(self, *args, **kwargs):
        """
        Plot samples from the frequency distribution
        displaying the most frequent sample first.  If an integer
        parameter is supplied, stop after this many samples have been
        plotted.  For a cumulative plot, specify cumulative=True.
        (Requires Matplotlib to be installed.)

        :param title: The title for the graph
        :type title: str
        :param cumulative: A flag to specify whether the plot is cumulative (default = False)
        :type title: bool
        """
        try:
            from matplotlib import pylab
        except ImportError:
            raise ValueError('The plot function requires matplotlib to be installed.'
                         'See http://matplotlib.org/')

        if len(args) == 0:
            args = [len(self)]
        samples = [item for item, _ in self.most_common(*args)]

        cumulative = _get_kwarg(kwargs, 'cumulative', False)
        if cumulative:
            freqs = list(self._cumulative_frequencies(samples))
            ylabel = "Cumulative Counts"
        else:
            freqs = [self[sample] for sample in samples]
            ylabel = "Counts"
        # percents = [f * 100 for f in freqs]  only in ProbDist?

        pylab.grid(True, color="silver")
        if not "linewidth" in kwargs:
            kwargs["linewidth"] = 2
        if "title" in kwargs:
            pylab.title(kwargs["title"])
            del kwargs["title"]
        pylab.plot(freqs, **kwargs)
        pylab.xticks(range(len(samples)), [compat.text_type(s) for s in samples], rotation=90)
        pylab.xlabel("Samples")
        pylab.ylabel(ylabel)
        pylab.show()