what is the failing test? please provide the full traceback. On 24 Jul 2017 10:58 pm, "Sam Barnett" <sambarnett95@gmail.com> wrote:
Dear scikit-learn developers,
I am developing a transformer, named Sqizer, that has the ultimate goal of modifying a kernel for use with the sklearn.svm package. When given an input data array X, Sqizer.transform(X) should have as its output the Gram matrix for X using the modified version of the kernel. Here is the code for the class so far:
class Sqizer(BaseEstimator, TransformerMixin):
def __init__(self, C=1.0, kernel='rbf', degree=3, gamma=1, coef0=0.0, cut_ord_pair=(2,1)): self.C = C self.kernel = kernel self.degree = degree self.gamma = gamma self.coef0 = coef0 self.cut_ord_pair = cut_ord_pair
def fit(self, X, y=None): # Check that X and y have correct shape X, y = check_X_y(X, y) # Store the classes seen during fit self.classes_ = unique_labels(y)
self.X_ = X self.y_ = y return self
def transform(self, X):
X = check_array(X, warn_on_dtype=True)
"""Returns Gram matrix corresponding to X, once sqized.""" def kPolynom(x,y): return (self.coef0+self.gamma*np.inner(x,y))**self.degree def kGauss(x,y): return np.exp(-self.gamma*np.sum(np.square(x-y))) def kLinear(x,y): return np.inner(x,y) def kSigmoid(x,y): return np.tanh(self.gamma*np.inner(x,y) +self.coef0)
def kernselect(kername): switcher = { 'linear': kPolynom, 'rbf': kGauss, 'sigmoid': kLinear, 'poly': kSigmoid, } return switcher.get(kername, "nothing")
cut_off = self.cut_ord_pair[0] order = self.cut_ord_pair[1]
from SeqKernel import hiSeqKernEval
def getGram(Y): gram_matrix = np.zeros((Y.shape[0], Y.shape[0])) for row1ind in range(Y.shape[0]): for row2ind in range
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