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--- a/README.md
+++ b/README.md
@@ -22,11 +22,10 @@
 * [Библиотека для автокодировщиков](labworks/LW2/lab02_lib.py) 
 
 
-### Лабораторная работа №3 31.10.2023 *(материалы 2022 года)*
+### Лабораторная работа №3 31.10.2023
 
-* [Задание](labworks/LW3/IS_Lab03_2022.pdf) 
-* [Методические указания](labworks/LW3/IS_Lab03_Metod_2022.pdf)
-* [Функция для отображения матрицы ошибок](labworks/LW3/cf_matrix.py) 
+* [Задание](labworks/LW3/IS_Lab03_2023.pdf) 
+* [Методические указания](labworks/LW3/IS_Lab03_Metod_2023.pdf)
 
 
 ### Лабораторная работа №4 28.11.2023 *(материалы 2022 года)*
diff --git a/labworks/LW3/IS_Lab03_2022.pdf b/labworks/LW3/IS_Lab03_2023.pdf
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diff --git a/labworks/LW3/cf_matrix.py b/labworks/LW3/cf_matrix.py
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--- a/labworks/LW3/cf_matrix.py
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@@ -1,119 +0,0 @@
-import numpy as np
-import matplotlib.pyplot as plt
-import seaborn as sns
-
-def make_confusion_matrix(cf,
-                          group_names=None,
-                          categories='auto',
-                          count=True,
-                          percent=True,
-                          cbar=True,
-                          xyticks=True,
-                          xyplotlabels=True,
-                          sum_stats=True,
-                          figsize=None,
-                          cmap='Blues',
-                          title=None,
-                          f_name=None,):
-    '''
-    This function will make a pretty plot of an sklearn Confusion Matrix cm using a Seaborn heatmap visualization.
-
-    Arguments
-    ---------
-    cf:            confusion matrix to be passed in
-
-    group_names:   List of strings that represent the labels row by row to be shown in each square.
-
-    categories:    List of strings containing the categories to be displayed on the x,y axis. Default is 'auto'
-
-    count:         If True, show the raw number in the confusion matrix. Default is True.
-
-    percent:       If True, show the proportions for each category. Default is True.
-
-    cbar:          If True, show the color bar. The cbar values are based off the values in the confusion matrix.
-                   Default is True.
-
-    xyticks:       If True, show x and y ticks. Default is True.
-
-    xyplotlabels:  If True, show 'True Label' and 'Predicted Label' on the figure. Default is True.
-
-    sum_stats:     If True, display summary statistics below the figure. Default is True.
-
-    figsize:       Tuple representing the figure size. Default will be the matplotlib rcParams value.
-
-    cmap:          Colormap of the values displayed from matplotlib.pyplot.cm. Default is 'Blues'
-                   See http://matplotlib.org/examples/color/colormaps_reference.html
-                   
-    title:         Title for the heatmap. Default is None.
-    
-    f_name:        Filename for saving picture. Default is None, which means no saving.
- 
-    '''
-
-
-    # CODE TO GENERATE TEXT INSIDE EACH SQUARE
-    blanks = ['' for i in range(cf.size)]
-
-    if group_names and len(group_names)==cf.size:
-        group_labels = ["{}\n".format(value) for value in group_names]
-    else:
-        group_labels = blanks
-
-    if count:
-        group_counts = ["{0:0.0f}\n".format(value) for value in cf.flatten()]
-    else:
-        group_counts = blanks
-
-    if percent:
-        group_percentages = ["{0:.2%}".format(value) for value in cf.flatten()/np.sum(cf)]
-    else:
-        group_percentages = blanks
-
-    box_labels = [f"{v1}{v2}{v3}".strip() for v1, v2, v3 in zip(group_labels,group_counts,group_percentages)]
-    box_labels = np.asarray(box_labels).reshape(cf.shape[0],cf.shape[1])
-
-
-    # CODE TO GENERATE SUMMARY STATISTICS & TEXT FOR SUMMARY STATS
-    if sum_stats:
-        #Accuracy is sum of diagonal divided by total observations
-        accuracy  = np.trace(cf) / float(np.sum(cf))
-
-        #if it is a binary confusion matrix, show some more stats
-        if len(cf)==2:
-            #Metrics for Binary Confusion Matrices
-            precision = cf[1,1] / sum(cf[:,1])
-            recall    = cf[1,1] / sum(cf[1,:])
-            f1_score  = 2*precision*recall / (precision + recall)
-            stats_text = "\n\nAccuracy={:0.3f}\nPrecision={:0.3f}\nRecall={:0.3f}\nF1 Score={:0.3f}".format(
-                accuracy,precision,recall,f1_score)
-        else:
-            stats_text = "\n\nAccuracy={:0.3f}".format(accuracy)
-    else:
-        stats_text = ""
-
-
-    # SET FIGURE PARAMETERS ACCORDING TO OTHER ARGUMENTS
-    if figsize==None:
-        #Get default figure size if not set
-        figsize = plt.rcParams.get('figure.figsize')
-
-    if xyticks==False:
-        #Do not show categories if xyticks is False
-        categories=False
-
-
-    # MAKE THE HEATMAP VISUALIZATION
-    plt.figure(figsize=figsize)
-    sns.heatmap(cf,annot=box_labels,fmt="",cmap=cmap,cbar=cbar,xticklabels=categories,yticklabels=categories,annot_kws={"size": 20})
-
-    if xyplotlabels:
-        plt.ylabel('True label')
-        plt.xlabel('Predicted label' + stats_text)
-    else:
-        plt.xlabel(stats_text)
-    
-    if title:
-        plt.title(title)
-    
-    if f_name:
-        plt.savefig(fname = f_name, dpi=None, facecolor='w', edgecolor='w', orientation='portrait', pad_inches=0.1)
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