Matlab - Transcribir codigo de Matlab a codigo java o python( Clasificador Bayes)

Sunny       Hot     High        Weak        No

Sunny       Hot     High        Strong      No

Overcast    Hot     High        Weak        Yes

Rain        Mild    High        Weak        Yes

Rain        Cool    Normal      Weak        Yes

Rain        Cool    Normal      Strong      No

Overcast    Cool    Normal      Strong      Yes

Sunny       Mild    High        Weak        No

Sunny       Cool    Normal      Weak        Yes

Rain        Mild    Normal      Weak        Yes

Sunny       Mild    Normal      Strong      Yes

Overcast    Mild    High        Strong      Yes

Overcast    Hot     Normal      Weak        Yes

Rain        Mild    High        Strong      No

Sunny,Hot,High,Weak

Sunny,Hot,High,Strong

Overcast,Hot,High,Weak

@02580808

Machine Learning - Naive Bayes Algorithm

No

No

Yes

disp("")

disp("Tacuma Solomon")

disp("@02580808")

disp("Machine Learning - Naive Bayes Algorithm")

disp("")

data = dlmread("train.dat",",");    		#Provides dimension information on the train.dat file

data2 = dlmread("test.dat",",");    		#Provieds dimension information on the test.dat file

#disp(data);

#disp("");

#disp(data2);

fid = fopen("train.dat");      	 		#Opens train.dat

for i=1:rows(data);				#for all rows of train.dat

  txt = fgetl(fid);				#Reads in information per row of the file

  A(i,:) = strsplit(txt,",",false); 		#Splits each line into a cell matrix A of strings

end

fclose(fid);					#Closes train.dat

fid = fopen("test.dat");			#Opens test.dat

for i=1:rows(data2)				#for all rows of train.dat

   txt = fgetl(fid);				#Reads in information per row of the file

   B(i,:) = strsplit(txt,",",false);		#Splits each line into a cell matrix B of strings

end

fclose(fid);									#Closes test.dat

for p = 1:rows(B)								# Loop For all the rows of Matrix B containing the test cases,

	YesNoProb = {0,0};							# Creates a matrix to hold the probabilities for Yes and No

	YesOrNo = {"Yes","No"};							# String matrix holding the strings "Yes" and "No"

	for h=1:columns(YesOrNo)						# Iterating twice for the yes and the no probability for each test case

		for l= 1:columns(B)

			Prob = {1:l}; 				                 # Creates a matrix holding the probabilities of each word per line

		end

		for i=1:columns(B)						 # Loop Calculateing each probability of each word per line

			count = 0;                             			 # Counter for (Selected Word) and "Yes" appearances

			count2 = 0;                            			 # Counter for "Yes" appearances

			for j=1:rows(A)						 # Loop iterating each row in the train.dat file, collecting the counts of each (Selected Word)

				z = strcmp(B{p,i},A{j,i});             		 # Compares (Selected Word) for matches in the train.dat array

				z2 = strcmp(YesOrNo{1,h},A{j,columns(A)});   	 # This counts the number of times "Yes/No" appears

				z=z+z2;

				if (z == 2) 					 #Increments count IF both (Selected Word) and "Yes/No" are encountered

				  count=count+1;

				endif

				if (z2 == 1)					 # Increments count IF Yes/No appears

				  count2=count2+1;

				endif

			end

			Prob{1,i} =  (count/count2);		    		 # Calculates the probablility of (Selected Word) being "Yes/No"

			if (Prob{1,i} == 0)					 # If (Selected Word) has no matches, IE. prob. is 0, automatically assign probability to 0.1

				Prob{1,i} = 0.1 ;

			endif

		end

		probyesno = count2/rows(A);					 # Calculates the probability(Yes/No)" 	

		calcprob = 1;

			for k = 1:columns(Prob)

				calcprob = (calcprob * Prob{1,k}); 		 # Loop multiplying the probabilities of each word

			end

		YesNoProb{1,h} = calcprob * probyesno;				 # Calculates the entire Yes/No probability of the test line 

	end

    if (YesNoProb{1,1} > YesNoProb{1,2})					 # If Yes probablility is greater than No probability

		disp("Yes");							 # Diisplay "Yes"

		else								 # Else,

		disp("No");							 # Display "No"

    endif

end										 # End Loop

disp("")

disp("")