my matlab version is 2012a. when I use fitcknn,has this error: Undefined function 'fitcknn' for input arguments of type 'cell'.

how to calculate probability for each class for predicate with knn without fitcknn?

after use this code, I want to calculate prob_estimates for each neighbors:

```
knn = ClassificationKNN.fit(trainsample, trainlabel,'NumNeighbors',7);
y = knn.predict(testsample);
```

Answer:

First, you have to know that `fitcknn`

& `ClassificationKNN.fit`

will end up with the same result. The difference is that fitcknn is a more recent version, so it allows more options.

As an example, if you use

```
load fisheriris;
X = meas;
Y = species;
```

Then this code will work

```
Mdl = fitcknn(X,Y,'NumNeighbors',5,'Standardize',1);
```

But this one won't

```
Mdl = ClassificationKNN.fit(X,Y,'NumNeighbors',5,'Standardize',1);
```

Because `ClassificationKNN.fit`

doesn't integrate the option `'Standardize'`

Here is the code to get the probability of each neighboor

```
knn = ClassificationKNN.fit(trainsample, trainlabel,'NumNeighbors',7);
predictions = knn.predict(testsample);
labels = unique(trainlabel);
num_occurrences = length(testsample);
labels_occurrences = [];
for label = labels
labels_occurrences = [label_occurrences, length(find(predictions == label))];
end
pro_estimates = label_occurrences ./ num_occurrences
```

Here's the explication of the code :

First, the classification itself

```
knn = ClassificationKNN.fit(trainsample, trainlabel,'NumNeighbors',7);
```

`knn`

will be an object of type ClassificationKNN, containing the classification of every sample

Now, the prediction.

`predict`

allows to predict the repartition of data depending on a classification model (here in your example the model is `knn`

)

```
predictions = knn.predict(testsample);
```

`predictions`

will contain the labels of the nearest neighboors from `knn`

of every data in `testsample`

.

So now we got the nearest neighboors of `testsample`

, we want to extract the probability of every classification in `knn`

.

We first need to extract the labels that we possibly can have.

```
labels = unique(trainlabel);
```

Then the number of cumulated occurrences of all the labels

```
num_occurrences = length(testsample);
```

Then we find the number of occurrences of every label in the nearest neighboors

```
labels_occurrences = []
for label = labels
labels_occurrences = [label_occurrences, length(find(predictions == label))];
end
```

To end, we just divide the number of occurrences of every label by the total number of occurrences to get a probability.

```
prob_estimates = label_occurrences ./ num_occurrences
```

That's it, you got the probability of every label.

Is this what you were looking for? Let me know!

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