baseGraph.py
def add_legend_bottom(self, title, mean, model, color='wheat', multimodal=False):
        """
        Generate a legend at the bottom of the graph
        args:
            title: type of measure (i.e:'coverage = ' or 'precision = ')
            mean: mean results for each discretization model (i.e: (0.7, 0.5) for a coverage of 0.7 for MDLP and 0.5 for Decile)
            model: Different discretization model that are measure
        """
        ax = plt.subplot(111)
        if not multimodal: 
            text = "Accuracy of the black box: " + str(self.accuracy)
            props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
            ax.text(0.05, 0.95, text, transform=ax.transAxes, fontsize=10, verticalalignment='top', bbox=props)
        for i in range(len(model)):
            text = (str(model[i]) + title + str(np.round(mean[i], 2)))
            line, = ax.plot(model[i], i, label=text)
        box = ax.get_position()
        ax.set_position([box.x0, box.y0 + box.height * 0.1, box.width, box.height*0.9])

        # Put a legend below current axis
        ax.legend(loc='upper center', bbox_to_anchor=(0.8, -0.05), fancybox=True, shadow=True, prop=self.fontP)

        Generate a legend at the bottom of the graph
        args:
            title: type of measure (i.e:'coverage = ' or 'precision = ')
            mean: mean results for each discretization model (i.e: (0.7, 0.5) for a coverage of 0.7 for MDLP and 0.5 for Decile)
            model: Different discretization model that are measure
        
Generate a legend at the bottom of the graph
args:
title: type of measure
mean: mean results for each discretization model  for a coverage of 0.7 for MDLP and 0.5 for Decile)
model: Different discretization model that are measure
Generate a legend at the bottom of the graph args : title : type of measure mean : mean results for each discretization model for a coverage of 0 . 7 for MDLP and 0 . 5 for Decile ) model : Different discretization model that are measure
def add_legend_bottom(self, title, mean, model, color='wheat', multimodal=False):
        ax = plt.subplot(111)
        if not multimodal: 
            text = "Accuracy of the black box: " + str(self.accuracy)
            props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
            ax.text(0.05, 0.95, text, transform=ax.transAxes, fontsize=10, verticalalignment='top', bbox=props)
        for i in range(len(model)):
            text = (str(model[i]) + title + str(np.round(mean[i], 2)))
            line, = ax.plot(model[i], i, label=text)
        box = ax.get_position()
        ax.set_position([box.x0, box.y0 + box.height * 0.1, box.width, box.height*0.9])
        ax.legend(loc='upper center', bbox_to_anchor=(0.8, -0.05), fancybox=True, shadow=True, prop=self.fontP)
def add_legend_bottom ( self , title , mean , model , color = 'wheat' , multimodal = False ) : ax = plt . subplot ( 111 ) if not multimodal : text = "Accuracy of the black box: " + str ( self . accuracy ) props = dict ( boxstyle = 'round' , facecolor = 'wheat' , alpha = 0.5 ) ax . text ( 0.05 , 0.95 , text , transform = ax . transAxes , fontsize = 10 , verticalalignment = 'top' , bbox = props ) for i in range ( len ( model ) ) : text = ( str ( model [ i ] ) + title + str ( np . round ( mean [ i ] , 2 ) ) ) line , = ax . plot ( model [ i ] , i , label = text ) box = ax . get_position ( ) ax . set_position ( [ box . x0 , box . y0 + box . height * 0.1 , box . width , box . height * 0.9 ] ) ax . legend ( loc = 'upper center' , bbox_to_anchor = ( 0.8 , - 0.05 ) , fancybox = True , shadow = True , prop = self . fontP )
Generate a legend at the bottom of the graph
args:
title: type of measure (i.e:'coverage = ' or 'precision = ')
mean: mean results for each discretization model (i.e: (0.7, 0.5) for a coverage of 0.7 for MDLP and 0.5 for Decile)
model: Different discretization model that are measure
Generate a legend at the bottom of the graph args : title : type of measure ( i . e : ' coverage = ' or ' precision = ' ) mean : mean results for each discretization model ( i . e : ( 0 . 7 0 . 5 ) for a coverage of 0 . 7 for MDLP and 0 . 5 for Decile ) model : Different discretization model that are measure
"""
        Generate a legend at the bottom of the graph
        args:
            title: type of measure (i.e:'coverage = ' or 'precision = ')
            mean: mean results for each discretization model (i.e: (0.7, 0.5) for a coverage of 0.7 for MDLP and 0.5 for Decile)
            model: Different discretization model that are measure
        """ # Put a legend below current axis
self
title
mean
model
color
multimodal