Using Kernels.Precomputed, svmpredict doesn't verify the dimensions of the input

[till-m]

Current behavior

It is possible to provide malformed gram matrices to svmpredict when using precomputed kernels:

using LIBSVM

# Training data
X = [-2 -1 -1 1 1 2;
     -1 -1 -2 1 2 1]
y = [1, 1, 1, 2, 2, 2]

# Testing data
T = [-1 2 3;
     -1 2 2]

# Precomputed matrix for training (corresponds to linear kernel)
K = X' * X

model = svmtrain(K, y, kernel=Kernel.Precomputed)

# Precomputed matrix for prediction
KK = X' * T

# truncate KK
KK_malformed = KK[1:1,:]

ỹ, decision_values = svmpredict(model, KK_malformed)

Output:

julia> ỹ
3-element Vector{Int64}:
 2
 2
 2

julia> decision_values
2×3 Matrix{Float64}:
 NaN    NaN    NaN
   0.0    0.0    0.0

Expected behavior

As described in the README, the gram matrix should have dimensions (l, n) when predicting n items on l training vectors and produce an error otherwise. Alternatively, it could also accept gram matrices of shape (k, n) where k is the number of support vectors of the model.

[till-m]

I've implemented the behavior as described above and added simple test cases. If you consider this a good change, let me know and I can make a pull request.

[iblislin]

Ah, PRs are welcomed :)