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

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

Publicado por Bryn (1 intervención) el 20/05/2014 07:21:51
hola a todos me gustaria compartir esto y aver si me pueden ayudar no tengo mucho conocimiento del lenguaje de matlab y tengo este codigo de un clasificador de bayes lo cual al leer un archivo train.dat donde hay estos datos:
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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

y un archivo test.dat donde estan estos datos (entrada)
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Sunny,Hot,High,Weak
Sunny,Hot,High,Strong
Overcast,Hot,High,Weak

la (salida) debe ser:
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@02580808
Machine Learning - Naive Bayes Algorithm
 
No
No
Yes

y este es el codigo que necesito convertirlo en codigo java o python cualqueira de lso 2 espero que me ayuden gracias :)



codigo:

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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("")
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