def generateKernelMatrix(DataSet):
"""Creates a kernel matrix/gram matrix from an input dataset, which is a list of examples"""
n_samples = len(DataSet)
kernelMatrix = np.empty([n_samples,n_samples], dtype = "string")
PatternIds = np.empty([n_samples,1],dtype = "string")
Labels = np.empty([n_samples,1],dtype = "string")
for i in xrange(n_samples):
(label, tps, pID, A1) = DataSet[i]
PatternIds[i,0] = pID
Labels[i,0] = label
for i in xrange(n_samples):
for j in xrange(n_samples):
(label1, tps, pID, A1) = DataSet[i]
(label2, tps, pID, A2) = DataSet[j]
kernelMatrix[i,j] = str(ger.GERPKernel(A1,A2))
kernelFileMatrix = np.concatenate(PatternIds, kernelMatrix)
labelMatrix = np.concatenate(PatternIds, Labels)
np.savetxt("labelText.txt", labelMatrix, delimiter = ',')
np.savetxt("kernelText.txt", kernelFileMatrix,delimiter=',')
labels = ml.Labels("labelText.txt")
kdata = ml.kernelData("kernelText.txt")
kdata.attachLabels(labels)
return kdata
def generateKernelMatrix(DataSet):
"""Creates a kernel matrix/gram matrix from an input dataset, which is a list of examples"""
n_samples = len(DataSet)
kernelMatrix = np.empty([n_samples, n_samples], dtype="string")
PatternIds = np.empty([n_samples, 1], dtype="string")
Labels = np.empty([n_samples, 1], dtype="string")
for i in xrange(n_samples):
(label, tps, pID, A1) = DataSet[i]
PatternIds[i, 0] = pID
Labels[i, 0] = label
for i in xrange(n_samples):
for j in xrange(n_samples):
(label1, tps, pID, A1) = DataSet[i]
(label2, tps, pID, A2) = DataSet[j]
kernelMatrix[i, j] = str(ger.GERPKernel(A1, A2))
kernelFileMatrix = np.concatenate(PatternIds, kernelMatrix)
labelMatrix = np.concatenate(PatternIds, Labels)
np.savetxt("labelText.txt", labelMatrix, delimiter=',')
np.savetxt("kernelText.txt", kernelFileMatrix, delimiter=',')
labels = ml.Labels("labelText.txt")
kdata = ml.kernelData("kernelText.txt")
kdata.attachLabels(labels)
return kdata
