def cross_validate(self, dataset, nr_fold):
"""
Perform stratified cross-validation to determine the
suitability of chosen model parameters.
Data are separated to nr_fold folds. Each fold is validated
against a model trained using the data from the remaining
(nr_fold-1) folds.
This function returns the percentage of data that was
classified correctly over all the experiments.
"""
problem = dataset._create_svm_problem()
target = N.empty((len(dataset.data),), dtype=N.float64)
tp = target.ctypes.data_as(POINTER(c_double))
libsvm.svm_cross_validation(problem, self.param, nr_fold, tp)
total_correct = 0.
for x, t in zip(dataset.data, target):
if x[0] == int(t):
total_correct += 1
# XXX also return results from folds in a list
return 100.0 * total_correct / len(dataset.data)
def cross_validate(self, dataset, nr_fold):
"""
Perform stratified cross-validation to determine the
suitability of chosen model parameters.
Data are separated to nr_fold folds. Each fold is validated
against a model trained using the data from the remaining
(nr_fold-1) folds.
This function returns the percentage of data that was
classified correctly over all the experiments.
"""
problem = dataset._create_svm_problem()
target = N.empty((len(dataset.data),), dtype=N.float64)
tp = target.ctypes.data_as(POINTER(c_double))
libsvm.svm_cross_validation(problem, self.param, nr_fold, tp)
total_correct = 0.0
for x, t in zip(dataset.data, target):
if x[0] == int(t):
total_correct += 1
# XXX also return results from folds in a list
return 100.0 * total_correct / len(dataset.data)
def cross_validate(self, dataset, nr_fold):
"""
Perform stratified cross-validation to determine the
suitability of chosen model parameters.
Data are separated to nr_fold folds. Each fold is validated
against a model trained using the data from the remaining
(nr_fold-1) folds.
This function returns a 2-tuple containing the mean squared
error and the squared correlation coefficient.
"""
problem = dataset._create_svm_problem()
target = N.empty((len(dataset.data),), dtype=N.float64)
tp = target.ctypes.data_as(POINTER(c_double))
libsvm.svm_cross_validation(problem, self.param, nr_fold, tp)
total_error = sumv = sumy = sumvv = sumyy = sumvy = 0.0
for i in range(len(dataset.data)):
v = target[i]
y = dataset.data[i][0]
sumv = sumv + v
sumy = sumy + y
sumvv = sumvv + v * v
sumyy = sumyy + y * y
sumvy = sumvy + v * y
total_error = total_error + (v - y) * (v - y)
# mean squared error
mse = total_error / len(dataset.data)
# squared correlation coefficient
l = len(dataset.data)
scc = ((l * sumvy - sumv * sumy) * (l * sumvy - sumv * sumy)) / (
(l * sumvv - sumv * sumv) * (l * sumyy - sumy * sumy)
)
return mse, scc
def cross_validate(self, dataset, nr_fold):
"""
Perform stratified cross-validation to determine the
suitability of chosen model parameters.
Data are separated to nr_fold folds. Each fold is validated
against a model trained using the data from the remaining
(nr_fold-1) folds.
This function returns a 2-tuple containing the mean squared
error and the squared correlation coefficient.
"""
problem = dataset._create_svm_problem()
target = N.empty((len(dataset.data), ), dtype=N.float64)
tp = target.ctypes.data_as(POINTER(c_double))
libsvm.svm_cross_validation(problem, self.param, nr_fold, tp)
total_error = sumv = sumy = sumvv = sumyy = sumvy = 0.
for i in range(len(dataset.data)):
v = target[i]
y = dataset.data[i][0]
sumv = sumv + v
sumy = sumy + y
sumvv = sumvv + v * v
sumyy = sumyy + y * y
sumvy = sumvy + v * y
total_error = total_error + (v - y) * (v - y)
# mean squared error
mse = total_error / len(dataset.data)
# squared correlation coefficient
l = len(dataset.data)
scc = ((l * sumvy - sumv * sumy) * (l * sumvy - sumv * sumy)) / \
((l * sumvv - sumv*sumv) * (l * sumyy - sumy * sumy))
return mse, scc
