我们从Python开源项目中，提取了以下50个代码示例，用于说明如何使用matplotlib.pylab.grid()。
def plot(traj, x, y, **kwargs): """ Create a matplotlib plot of property x against property y Args: x,y (str): names of the properties **kwargs (dict): kwargs for :meth:`matplotlib.pylab.plot` Returns: List[matplotlib.lines.Lines2D]: the lines that were plotted """ from matplotlib import pylab xl = yl = None if type(x) is str: strx = x x = getattr(traj, x) xl = '%s / %s' % (strx, getattr(x, 'units', 'dimensionless')) if type(y) is str: stry = y y = getattr(traj, y) yl = '%s / %s' % (stry, getattr(y, 'units', 'dimensionless')) plt = pylab.plot(x, y, **kwargs) pylab.xlabel(xl); pylab.ylabel(yl); pylab.grid() return plt
def plot_confusion_matrix(cm, label_list, title='Confusion matrix', cmap=None): from matplotlib import pylab cm = np.asarray(cm, dtype=np.float32) for i, row in enumerate(cm): cm[i] = cm[i] / np.sum(cm[i]) #import matplotlib.pyplot as plt #plt.ion() pylab.clf() pylab.matshow(cm, fignum=False, cmap='Blues', vmin=0, vmax=1.0) ax = pylab.axes() ax.set_xticks(range(len(label_list))) ax.set_xticklabels(label_list, rotation='vertical') ax.xaxis.set_ticks_position('bottom') ax.set_yticks(range(len(label_list))) ax.set_yticklabels(label_list) pylab.title(title) pylab.colorbar() pylab.grid(False) pylab.xlabel('Predicted class') pylab.ylabel('True class') pylab.grid(False) pylab.savefig('test.jpg') pylab.show()
def plot_entropy(): pylab.clf() pylab.figure(num=None, figsize=(5, 4)) title = "Entropy $H(X)$" pylab.title(title) pylab.xlabel("$P(X=$coin will show heads up$)$") pylab.ylabel("$H(X)$") pylab.xlim(xmin=0, xmax=1.1) x = np.arange(0.001, 1, 0.001) y = -x * np.log2(x) - (1 - x) * np.log2(1 - x) pylab.plot(x, y) # pylab.xticks([w*7*24 for w in [0,1,2,3,4]], ['week %i'%(w+1) for w in # [0,1,2,3,4]]) pylab.autoscale(tight=True) pylab.grid(True) filename = "entropy_demo.png" pylab.savefig(os.path.join(CHART_DIR, filename), bbox_inches="tight")
def plot_clustering(x, y, title, mx=None, ymax=None, xmin=None, km=None): pylab.figure(num=None, figsize=(8, 6)) if km: pylab.scatter(x, y, s=50, c=km.predict(list(zip(x, y)))) else: pylab.scatter(x, y, s=50) pylab.title(title) pylab.xlabel("Occurrence word 1") pylab.ylabel("Occurrence word 2") pylab.autoscale(tight=True) pylab.ylim(ymin=0, ymax=1) pylab.xlim(xmin=0, xmax=1) pylab.grid(True, linestyle='-', color='0.75') return pylab
def plot_confusion_matrix(cm, genre_list, name, title): pylab.clf() pylab.matshow(cm, fignum=False, cmap='Blues', vmin=0, vmax=1.0) ax = pylab.axes() ax.set_xticks(range(len(genre_list))) ax.set_xticklabels(genre_list) ax.xaxis.set_ticks_position("bottom") ax.set_yticks(range(len(genre_list))) ax.set_yticklabels(genre_list) pylab.title(title) pylab.colorbar() pylab.grid(False) pylab.show() pylab.xlabel('Predicted class') pylab.ylabel('True class') pylab.grid(False) pylab.savefig( os.path.join(CHART_DIR, "confusion_matrix_%s.png" % name), bbox_inches="tight")
def plot_roc(auc_score, name, tpr, fpr, label=None): pylab.clf() pylab.figure(num=None, figsize=(5, 4)) pylab.grid(True) pylab.plot([0, 1], [0, 1], 'k--') pylab.plot(fpr, tpr) pylab.fill_between(fpr, tpr, alpha=0.5) pylab.xlim([0.0, 1.0]) pylab.ylim([0.0, 1.0]) pylab.xlabel('False Positive Rate') pylab.ylabel('True Positive Rate') pylab.title('ROC curve (AUC = %0.2f) / %s' % (auc_score, label), verticalalignment="bottom") pylab.legend(loc="lower right") filename = name.replace(" ", "_") pylab.savefig( os.path.join(CHART_DIR, "roc_" + filename + ".png"), bbox_inches="tight")
def plotKChart(self, misClassDict, saveFigPath): kList = [] misRateList = [] for k, misClassNum in misClassDict.iteritems(): kList.append(k) misRateList.append(1.0 - 1.0/k*misClassNum) fig = plt.figure(saveFigPath) plt.plot(kList, misRateList, 'r--') plt.title(saveFigPath) plt.xlabel('k Num.') plt.ylabel('Misclassified Rate') plt.legend(saveFigPath) plt.grid(True) plt.savefig(saveFigPath) plt.show() ################################### PART3 TEST ######################################## # ??
def plot_penalty_vl(debug, tag, fold_exp): plt.close("all") vl = np.array(debug["penalty"]) fig = plt.figure(figsize=(15, 10.8), dpi=300) names = debug["names"] for i in range(vl.shape[1]): if vl.shape[1] > 1: plt.plot(vl[:, i], label="layer_"+str(names[i])) else: plt.plot(vl[:], label="layer_"+str(names[i])) plt.xlabel("mini-batchs") plt.ylabel("value of penlaty") plt.title( "Penalty value over layers:" + "_".join([str(k) for k in names]) + ". tag:" + tag) plt.legend(loc='upper right', fancybox=True, shadow=True, prop={'size': 8}) plt.grid(True) fig.savefig(fold_exp+"/penalty.png", bbox_inches='tight') plt.close('all') del fig
def plot_confusion_matrix(cm, plot_title, filename, genres=None): if not genres: genres = GENRES pylab.clf() pylab.matshow(cm, fignum=False, cmap='Blues', vmin=0, vmax=100.0) axes = pylab.axes() axes.set_xticks(range(len(genres))) axes.set_xticklabels(genres, rotation=45) axes.set_yticks(range(len(genres))) axes.set_yticklabels(genres) axes.xaxis.set_ticks_position("bottom") pylab.title(plot_title, fontsize=14) pylab.colorbar() pylab.xlabel('Predicted class', fontsize=12) pylab.ylabel('Correct class', fontsize=12) pylab.grid(False) #pylab.show() pylab.savefig(os.path.join(PLOTS_DIR, "cm_%s.eps" % filename), bbox_inches="tight")
def plot_forest_all_proba(y_proba_all, y_gt): from matplotlib import pylab N = len(y_gt) num_tree = len(y_proba_all) pylab.clf() mat = np.zeros((num_tree, N)) LOGGER.info('mat.shape={}'.format(mat.shape)) for i in range(num_tree): mat[i,:] = y_proba_all[i][(range(N), y_gt)] pylab.matshow(mat, fignum=False, cmap='Blues', vmin=0, vmax=1.0) pylab.grid(False) pylab.show()
def imageSegy(Data): """ imageSegy(Data) Image segy Data """ import matplotlib.pylab as plt plt.imshow(Data) plt.title('pymat test') plt.grid(True) plt.show() #%%
def wiggle(Data,SH,skipt=1,maxval=8,lwidth=.1): """ wiggle(Data,SH) """ import matplotlib.pylab as plt t = range(SH['ns']) # t = range(SH['ns'])*SH['dt']/1000000; for i in range(0,SH['ntraces'],skipt): # trace=zeros(SH['ns']+2) # dtrace=Data[:,i] # trace[1:SH['ns']]=Data[:,i] # trace[SH['ns']+1]=0 trace=Data[:,i] trace[0]=0 trace[SH['ns']-1]=0 plt.plot(i+trace/maxval,t,color='black',linewidth=lwidth) for a in range(len(trace)): if (trace[a]<0): trace[a]=0; # pylab.fill(i+Data[:,i]/maxval,t,color='k',facecolor='g') plt.fill(i+Data[:,i]/maxval,t,'k',linewidth=0) plt.title(SH['filename']) plt.grid(True) plt.show() #%%
def plot_pr(auc_score, name, phase, precision, recall, label=None): pylab.clf() pylab.figure(num=None, figsize=(5, 4)) pylab.grid(True) pylab.fill_between(recall, precision, alpha=0.5) pylab.plot(recall, precision, lw=1) pylab.xlim([0.0, 1.0]) pylab.ylim([0.0, 1.0]) pylab.xlabel('Recall') pylab.ylabel('Precision') pylab.title('P/R curve (AUC=%0.2f) / %s' % (auc_score, label)) filename = name.replace(" ", "_") pylab.savefig(os.path.join(CHART_DIR, "pr_%s_%s.png" % (filename, phase)), bbox_inches="tight")
def plot_log(): pylab.clf() pylab.figure(num=None, figsize=(6, 5)) x = np.arange(0.001, 1, 0.001) y = np.log(x) pylab.title('Relationship between probabilities and their logarithm') pylab.plot(x, y) pylab.grid(True) pylab.xlabel('P') pylab.ylabel('log(P)') filename = 'log_probs.png' pylab.savefig(os.path.join(CHART_DIR, filename), bbox_inches="tight")
def plot_feat_hist(data_name_list, filename=None): pylab.clf() 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('Density') # 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, bins=bins, normed=1, facecolor='green', alpha=0.75) pylab.grid(True) if not filename: filename = "feat_hist_%s.png" % name pylab.savefig(os.path.join(CHART_DIR, filename), bbox_inches="tight")
def plot_bias_variance(data_sizes, train_errors, test_errors, name): pylab.clf() 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, train_errors, "-", data_sizes, test_errors, "--", lw=1) pylab.legend(["train error", "test error"], loc="upper right") pylab.grid() pylab.savefig(os.path.join(CHART_DIR, "bv_" + name + ".png"))
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 = int(1 + (len(data_name_list) - 1) / 2) num_cols = int(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, bins=bins, normed=1, 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(["test error", "train 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(["test error", "train 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_log(): pylab.clf() x = np.arange(0.001, 1, 0.001) y = np.log(x) pylab.title('Relationship between probabilities and their logarithm') pylab.plot(x, y) pylab.grid(True) pylab.xlabel('P') pylab.ylabel('log(P)') filename = 'log_probs.png' pylab.savefig(os.path.join(CHART_DIR, filename), bbox_inches="tight")
def plot_bias_variance(data_sizes, train_errors, test_errors, name): pylab.clf() 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, train_errors, "-", data_sizes, test_errors, "--", lw=1) pylab.legend(["train error", "test error"], loc="upper right") pylab.grid(True) pylab.savefig(os.path.join(CHART_DIR, "bv_" + name + ".png"))
def plot_debug_grad(debug, tag, fold_exp, trg): plt.close("all") # f = plt.figure(figsize=(15, 10.8), dpi=300) nbr_rows = int(len(debug["grad_sup"][0])/2) f, axs = plt.subplots(nbr_rows, 2, sharex=True, sharey=False, figsize=(15, 12.8), dpi=300) if trg == "sup": grad = np.array(debug["grad_sup"]) elif trg == "hint": grad = np.array(debug["grad_hint"]) print grad.shape, trg j = 0 for i in range(0, nbr_rows*2, 2): w_vl = grad[:, i] b_vl = grad[:, i+1] axs[j, 0].plot(w_vl, label=trg) axs[j, 0].set_title("w"+str(j)) axs[j, 1].plot(b_vl, label=trg) axs[j, 1].set_title("b"+str(j)) axs[j, 0].grid(True) axs[j, 1].grid(True) j += 1 f.suptitle("Grad sup/hint:" + tag, fontsize=8) plt.legend() f.savefig(fold_exp+"/grad_" + trg + ".png", bbox_inches='tight') plt.close("all") del f
def plot_debug_ratio_grad(debug, fold_exp, r="h/s"): plt.close("all") # f = plt.figure(figsize=(15, 10.8), dpi=300) nbr_rows = int(len(debug["grad_sup"][0])/2) f, axs = plt.subplots(nbr_rows, 2, sharex=True, sharey=False, figsize=(15, 12.8), dpi=300) grads = np.array(debug["grad_sup"]) gradh = np.array(debug["grad_hint"]) if gradh.size != grads.size: print "Can't calculate the ratio. It looks like you divided the " +\ "hint batch..." return 0 print gradh.shape, grads.shape j = 0 for i in range(0, nbr_rows*2, 2): w_vls = grads[:, i] b_vls = grads[:, i+1] w_vl_h = gradh[:, i] b_vlh = gradh[:, i+1] if r == "h/s": ratio_w = np.divide(w_vl_h, w_vls) ratio_b = np.divide(b_vlh, b_vls) elif r == "s/h": ratio_w = np.divide(w_vls, w_vl_h) ratio_b = np.divide(b_vls, b_vlh) else: raise ValueError("Either h/s or s/h.") axs[j, 0].plot(ratio_w, label=r) axs[j, 0].set_title("w"+str(j)) axs[j, 1].plot(ratio_b, label=r) axs[j, 1].set_title("b"+str(j)) axs[j, 0].grid(True) axs[j, 1].grid(True) j += 1 f.suptitle("Ratio gradient: " + r, fontsize=8) plt.legend() f.savefig(fold_exp+"/ratio_grad_" + r.replace("/", "-") + ".png", bbox_inches='tight') plt.close("all") del f
def plot_roc(auc_score, name, fpr, tpr): pylab.figure(num=None, figsize=(6, 5)) pylab.plot([0, 1], [0, 1], 'k--') pylab.xlim([0.0, 1.0]) pylab.ylim([0.0, 1.0]) pylab.xlabel('False Positive Rate') pylab.ylabel('True Positive Rate') pylab.title('Receiver operating characteristic (AUC=%0.2f)\n%s' % ( auc_score, name)) pylab.legend(loc="lower right") pylab.grid(True, linestyle='-', color='0.75') pylab.fill_between(tpr, fpr, alpha=0.5) pylab.plot(fpr, tpr, lw=1) pylab.savefig(os.path.join(CHART_DIR, "roc_" + name.replace(" ", "_")+ ".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()
