I have a training sample set and the according targets. The targets are 7 different classes.
net = patternnet(10);
[net,tr] = train(net,samples.',targets.');
%samples is 200x7 and targets is 200x1
test = net([0;1;1;1;1;0;1])
Now when I execute the last line I would expect test to hold the value of the class that the net classifies the input [0;1;1;1;1;0;1] as. But All I get is a weird decimal value, and not one of the values that are entered in "targets" (which are 1,2,3,4,5,6 or 7). What am I doing wrong?
The patternnet documentation says that
The target data for pattern recognition networks should consist of vectors of all zero values except for a 1 in element i, where i is the class they are to represent.
Thus, targets should be an array of the size 200x7, where 7 is the number of the classes. Each column should consist of zeros except for the i-th row, where i is the index of the class.
You can transform targets to the required form by using the ind2vec function.
Related
I am tuning an SVM using a for loop to search in the range of hyperparameter's space. The svm model learned contains the following fields
SVMModel: [1×1 ClassificationSVM]
C: 2
FeaturesIdx: [4 6 8]
Score: 0.0142
Question1) What is the meaning of the field 'score' and its utility?
Question2) I am tuning the BoxConstraint, C value. Let, the number of features be denoted by the variable featsize. The variable gridC will contain the search space which can start from any value say 2^-5, 2^-3, to 2^15 etc. So, gridC = 2.^(-5:2:15). I cannot understand if there is a way to select the range?
1. score had been documented in here, which says:
Classification Score
The SVM classification score for classifying observation x is the signed distance from x to the decision boundary ranging from -∞ to +∞.
A positive score for a class indicates that x is predicted to be in
that class. A negative score indicates otherwise.
In two class cases, if there are six observations, and the predict function gave us some score value called TestScore, then we could determine which class does the specific observation ascribed by:
TestScore=[-0.4497 0.4497
-0.2602 0.2602;
-0.0746 0.0746;
0.1070 -0.1070;
0.2841 -0.2841;
0.4566 -0.4566;];
[~,Classes] = max(TestScore,[],2);
In the two-class classification, we can also use find(TestScore > 0) instead, and it is clear that the first three observations are belonging to the second class, and the 4th to 6th observations are belonging to the first class.
In multiclass cases, there could be several scores > 0, but the code max(scores,[],2) is still validate. For example, we could use the code (from here, an example called Find Multiple Class Boundaries Using Binary SVM) following to determine the classes of the predict Samples.
for j = 1:numel(classes);
[~,score] = predict(SVMModels{j},Samples);
Scores(:,j) = score(:,2); % Second column contains positive-class scores
end
[~,maxScore] = max(Scores,[],2);
Then the maxScore will denote the predicted classes of each sample.
2. The BoxConstraint denotes C in the SVM model, so we can train SVMs in different hyperparameters and select the best one by something like:
gridC = 2.^(-5:2:15);
for ii=1:length(gridC)
SVModel = fitcsvm(data3,theclass,'KernelFunction','rbf',...
'BoxConstraint',gridC(ii),'ClassNames',[-1,1]);
%if (%some constraints were meet)
% %save the current SVModel
%end
end
Note: Another way to implement this is using libsvm, a fast and easy-to-use SVM toolbox, which has the interface of MATLAB.
I'm working on gait recognition problem, the aim of this study is to be used for user authentication
I have data of 36 users
I've successfully extracted 143 features for each sample (or example) which are (36 rows and 143 columns) for each user
( in other words, I have 36 examples and 143 features are extracted for each example. Thus a matrix called All_Feat of 36*143 has been created for each individual user).
By the way, column represents the number of the extracted features and row represents the number of samples (examples) for each feature.
Then I have divided the data into two parts, training and testing (the training matrix contains 25 rows and 143 columns, while the testing Matrix contains 11 rows and 143 columns).
Then, for each user, I divided the matrix (All_Feat) into two matrixes ( Training matrix, and Test matrix ).
The training matrix contains ( 25 rows (examples) and 143 columns), while the testing matrix has (11 rows and 143 columns).
I'm new to classification and this stuff
I'd like to use machine learning (Neural Network) for classifying these features.
Therefore, the first step I need to create a reference template for each user ( which called training phase)
this can be done by training the classifier with the user's features (data) and the remaining Users as well (35 users are considered as imposters).
Based on what I have read, training Neural Network requires two classes, the first class contains all the training data of genuine user (e.g. User1) and labelled with 1 , while the second class has the training data of imposters labelled as 0 (which is binary classification, 1 for the authorised user and 0 for imposters).
**now my question is: **
1- i dont know how to create these classes!
2- For example, if I want to train Neural Network for User1, I have these variables, input and target. what should I assign to these variables?
should input= Training matrix of User1 and Training matrixes of User2, User3,.....User35 ?
Target=what should i assign to this matrix?
I really appreciate any help!
Try this: https://es.mathworks.com/help/nnet/gs/classify-patterns-with-a-neural-network.html
A few notes:
You said that, for each user, you have extracted 136 features. It sounds like you have only one repetition for each user (i.e. the user has tried-used the system once). However, I don't know the source of your data, but I dunno that it hasn't got some type of randomness. You mention gait analysis, and that sounds like that the recorded data of a given one user will be different each time that user uses the system. In other words: the user uses your system, you capture the data, you extract the 136 features (numbers); then, the user uses again the system, but the extracted 136 features will be slightly different. Therefore, you should get several examples for each user to train the classifier. In terms of "matlab matrix" your matrix should have one COLUMN for each example, and 136 rows (each of you features). Since you should have several repetitions for each user (for example 10 times), your big matrix should be something like: 136 rows x 360 columns.
You should "create" one new neural network for each user. Given a user (for example User4), you create a dataset (a new matrix) with samples of that user, and samples of several other users (User1, User3, User5...). You do a binary classification (cases: "user4" against "other users"). After training, it would be advisable to test the classifier with data of other users whose data was not present during the training phase (for example User2 and others). Since you are doing a binary classification your matrices should be somthing like follows:
Example, you have 10 trials (examples) of each user. You want to create a neural network to detect the user User1. The matrix should be like:
(notation cU1_t1 means: column with features of user 1, trial 1)
input_matrix = [cU1_t1; cU1_t2; ...; cU1_t10; cU2_t1; ...; cU36_t10]
The target matrix should be like:
target = a matrix whose 10 first columns are [ 1, 0], and the other 350 columns are [0, 1]. That means that the first 10 columns are of type A, and the others of type B. In this case "type A" means "User1", and "type B" means "Not User1".
Then, you should segment the data (train data, validation data, test data) to train the nerual network and so on. Remember to save some users just for the testing phase, for example, the train matrix should not have any of the columns of five users: user2, user6, user7, user10, user20 (50 columns).
I think you get the idea.
Regards.
************ UPDATE: ******************************
This example assumes that the user selects/indicates its name and then the system uses the neural network to authenticate the user (like a password). I will give you an small example with random numbers.
Let's say you have recorded data from 15 users (but in the future you will have more). You record "gait data" from them when they do something with your recording device. From the recorded signals you extract some features, let's say you extract 5 features (5 numbers). Hence, everytime a user uses the machine you get 5 numbers. Even if user is the same, the 5 numbers will be different each time, because the recorded signals have some randomness. Therefore, to train the neural network you have to have several examples of each user. Let's say that you have 18 repetitions performed by each user.
To sum up this example:
There are 15 users available for the experiment.
Each time the user uses the system you record 5 numbers (features). You get a feature vector. In matlab it will be a COLUMN.
For the experiment each user has performed 18 repetitions.
Now you have to create one neural network for each user. To that end, you have to construct several matrices.
Let's say you want to create the neural network (NN) of user 2 (U2). The NN will classify the feature vectors in 2 classes: U2 and NotU2. Therefore, you have to train and test the NN with examples of this. The group NotU2 represents any other user that it is not U2, however, you should NOT train the NN with data of every other user that you have in your experiment. This will be cheating (think that you can't have data from every user in the world). Therefore, to create the train dataset you will exclude all the repetitions of some users to test the NN during the training (validation dataset) and after the trainning (test dataset). For this example we will use users {U1,U3,U4} for validation, and users {U5,U6,U7} for testing.
Therefore you construct the following matrices:
Train input matrix
It wil have 12 examples of U2 (70% more or less) and every example of users {U8,U9,...,U14,U15}. Each example is a column, hence, the train matrix will be a matrix of 5 rows and 156 columns (12+8*18). I will order it as follows: [U2_ex1, U2_ex2, ..., U2_ex12, U8_ex1, U8_ex2, ..., U8_ex18, U9_ex1, ..., U15_ex1,...U15_ex18]. Where U2_ex1 represents a column vector with the 5 features obtained of User 2 during the repetition/example number 1.
-- Target matrix of train matrix. It is a matrix of 2 rows and 156 columns. Each column j represents the correct class of the example j. The column is formed by zeros, and it has a 1 at the row that indicates the class. Since we have only 2 classes the matrix has only 2 rows. I will say that class U2 will be the first one (hence the column vector for each example of this class will be [1 0]), and the other class (NotU2) will be the second one (hence the column vector for each example of this class will be [0 1]). Obviously, the columns of this matrix have the same order than the train matrix. So, according to the order that I have used, the target matrix will be:
12 columns [1 0] and 144 columns [0 1].
Validation input matrix
It will have 3 examples of U2 (15% more or less) and every example of users [U1,U3,U4]. Hence, this will be a matrix of 6 rows and 57 columns ( 3+3*18).
-- Target matrix of validation matrix: A matrix of 2 rows and 57 columns: 3 columns [1 0] and 54 columns [0 1].
Test input matrix
It will have the remaining 3 examples of U2 (15%) and every example of users [U5,U6,U7]. Hence, this will be a matrix of 6 rows and 57 columns (3+3*18).
-- Target matrix of test matrix: A matrix of 2 rows and 57 columns: 3 columns [1 0] and 54 columns [0 1].
IMPORTANT. The columns of each matrix should have a random order to improve the training. That is, do not put all the examples of U2 together and then the others. For this example I have put them in order for clarity. Obviously, if you change the order of the input matrix, you have to use the same order in the target matrix.
To use MATLAB you will have to to pass two matrices: the inputMatrix and the targetMatrix. The inputMatrix will have the train,validation and test input matrices joined. And the targetMatrix the same with the targets. So, the inputMatrix will be a matrix of 6 rows and 270 columns. The targetMatrix will have 2 rows and 270 columns. For clarity I will say that the first 156 columns are the trainning ones, then the 57 columns of validation, and finally 57 columns of testing.
The MATLAB commands will be:
% Create a Pattern Recognition Network
hiddenLayerSize = 10; %You can play with this number
net = patternnet(hiddenLayerSize);
%Specify the indices of each matrix
net.divideFcn = 'divideind';
net.divideParam.trainInd = [1: 156];
net.divideParam.valInd = [157:214];
net.divideParam.testInd = [215:270];
% % Train the Network
[net,tr] = train(net, inputMatrix, targetMatrix);
In the open window you will be able to see the performance of your neural network. The output object "net" is your neural network trained. You can use it with new data if you want.
Repeat this process for each other user (U1, U3, ...U15) to obtain his/her neural network.
Hi I have been using Murphy's HMM toolbox with output of Gaussian Mixture. In brief, I have 2 datasets for training. Each dataset comprises of 2000 observations with 11 dimensions per observation. I implemented the following steps to observe the path sequence output.
N_states=2
N_Gaussian_Mixture=1
For each of the dataset, a HMM model was generated. The steps are:
Step 1: mixgauss_init() was used to generated GMM signature for my training data.
Step 2: After declaring the matrices for Prior and Transmat, mhmm_em() was used to generate HMM model for the training dataset.
Testing: 2 test data from each of the dataset are used for testing using mhm_logprob(). The output were correctly predicted using loglikelihood scores in every run.
However, when I tried to observe the sequence of the HMM modelling (Dataset_123 with testdata_123) via mixgauss_prob() followed by viterbi_path(), the output sequences were inconsistent. For example, for the first run, the output sequence can be 2221111111111. But when I rerun the program again, the sequence can change to 1111111111111 or 1111111111222. Initially I thought it could be due to my Prior matrix. I fixed the Prior value but it is not helping.
Secondly, it there a possibility when I can assigned labels to the states and sequence? Like Matlab function:
hmmgenerate(...,'Symbols',SYMBOLS) specifies the symbols that are emitted. SYMBOLS can be a numeric array or a cell array of the names of the symbols. The default symbols are integers 1 through N, where N is the number of possible emissions.
`hmmgenerate(...,'Statenames',STATENAMES) specifies the names of the states. STATENAMES can be a numeric array or a cell array of the names of the states. The default state names are 1 through M, where M is the number of states.?
Thank you for your time and hope to hear from the expert sharing.
I have to write a script using MATLAB which will classify my data.
My data consists of 1051 web pages (rows) and 11000+ words (columns). I am holding the word occurences in the matrix for each page. The first 230 rows are about computer science course (to be labeled with +1) and remaining 821 are not (to be labeled with -1). I am going to label few part of these rows (say 30 rows) by myself. Then SVM will label the remaining unlabeled rows.
I have found that I could solve my problem using MATLAB's svmtrain() and svmclassify() methods. First I need to create SVMStruct.
SVMStruct = svmtrain(Training,Group)
Then I need to use
Group = svmclassify(SVMStruct,Sample)
But the point that I do not know what Training and Group are. For Group Mathworks says:
Grouping variable, which can be a categorical, numeric, or logical
vector, a cell vector of strings, or a character matrix with each row
representing a class label. Each element of Group specifies the group
of the corresponding row of Training. Group should divide Training
into two groups. Group has the same number of elements as there are
rows in Training. svmtrain treats each NaN, empty string, or
'undefined' in Group as a missing value, and ignores the corresponding
row of Training.
And for Training it is said that:
Matrix of training data, where each row corresponds to an observation
or replicate, and each column corresponds to a feature or variable.
svmtrain treats NaNs or empty strings in Training as missing values
and ignores the corresponding rows of Group.
I want to know how I can adopt my data to Training and Group? I need (at least) a little code sample.
EDIT
What I did not understand is that in order to have SVMStruct I have to run
SVMStruct = svmtrain(Training, Group);
and in order to have Group I have to run
Group = svmclassify(SVMStruct,Sample);
Also I still did not get what Sample should be like?
I am confused.
Training would be a matrix with 1051 rows (the webpages/training instances) and 11000 columns (the features/words). I'm assuming you want to test for the existence of each word on a webpage? In this case you could make the entry of the matrix a 1 if the word exists for a given webpage and a 0 if not.
You could initialize the matrix with Training = zeros(1051,11000); but filling the entries would be up to you, presumably done with some other code you've written.
Group is a 1-D column vector with one entry for every training instance (webpage) than tells you which of two classes the webpage belongs to. In your case you would make the first 230 entries a "+1" for computer science and the remaining 821 entries a "-1" for not.
Group = zeros(1051,1); % gives you a matrix of zeros with 1051 rows and 1 column
Group(1:230) = 1; % set first 230 entries to +1
Group(231:end) = -1; % set the rest to -1
I've got a problem with implementing multilayered perceptron with Matlab Neural Networks Toolkit.
I try to implement neural network which will recognize single character stored as binary image(size 40x50).
Image is transformed into a binary vector. The output is encoded in 6bits. I use simple newff function in that way (with 30 perceptrons in hidden layer):
net = newff(P, [30, 6], {'tansig' 'tansig'}, 'traingd', 'learngdm', 'mse');
Then I train my network with a dozen of characters in 3 different fonts, with following train parameters:
net.trainParam.epochs=1000000;
net.trainParam.goal = 0.00001;
net.traxinParam.lr = 0.01;
After training net recognized all characters from training sets correctly but...
It cannot recognize more then twice characters from another fonts.
How could I improve that simple network?
you can try to add random elastic distortion to your training set (in order to expand it, and making it more "generalizable").
You can see the details on this nice article from Microsoft Research :
http://research.microsoft.com/pubs/68920/icdar03.pdf
You have a very large number of input variables (2,000, if I understand your description). My first suggestion is to reduce this number if possible. Some possible techniques include: subsampling the input variables or calculating informative features (such as row and column total, which would reduce the input vector to 90 = 40 + 50)
Also, your output is coded as 6 bits, which provides 32 possible combined values, so I assume that you are using these to represent 26 letters? If so, then you may fare better with another output representation. Consider that various letters which look nothing alike will, for instance, share the value of 1 on bit 1, complicating the mapping from inputs to outputs. An output representation with 1 bit for each class would simplify things.
You could use patternnet instead of newff, this creates a network more suitable for pattern recognition. As target function use a 26-elements vector with 1 in the right letter's position (0 elsewhere). The output of the recognition will be a vector of 26 real values between 0 and 1, with the recognized letter with the highest value.
Make sure to use data from all fonts for the training.
Give as input all data sets, train will automatically divide them into train-validation-test sets according to the specified percentages:
net.divideParam.trainRatio = .70;
net.divideParam.valRatio = .15;
net.divideParam.testRatio = .15;
(choose you own percentages).
Then test using only the test set, you can find their indices into
[net, tr] = train(net,inputs,targets);
tr.testInd