def svm_hmp_2_feature_plot():
print('hazzah!')
shared_file_path = '/home/jlynch/gsoc2013/data/Stool.0.03.subsample.0.03.filter.shared'
design_file_path = '/home/jlynch/gsoc2013/data/Stool.0.03.subsample.0.03.filter.mix.design'
shared_data = mothur_files.load_shared_file(shared_file_path)
design_data = mothur_files.load_design_file(design_file_path)
otu1 = 'Otu29878'
otu2 = 'Otu29552'
# where are Otu29741 and Otu29678
n_otu1 = shared_data.otu_column_names.index(otu1)
n_otu2 = shared_data.otu_column_names.index(otu2)
print('{} is on column {}'.format(otu1, n_otu1))
print('{} is on column {}'.format(otu2, n_otu2))
print('shape of design_data.class_number_for_row {}'.format(
design_data.class_number_for_row.shape))
class_zero = design_data.class_number_for_row == 2.0
class_one = design_data.class_number_for_row == 1.0
print('class zero count: {}'.format(np.sum(class_zero)))
print('class one count: {}'.format(np.sum(class_one)))
two_labels = np.logical_or(class_zero, class_one)
print('shape of two_labels: {}'.format(two_labels.shape))
label_index = np.arange(design_data.class_number_for_row.shape[0])
reduced_label_index = label_index[two_labels[:, 0]]
print('reduced_label_index: {}'.format(reduced_label_index))
two_labels_otu_frequency = shared_data.otu_frequency[
reduced_label_index, :]
print('shape of two_labels_otu_frequency: {}'.format(
two_labels_otu_frequency.shape))
reduced_otu_frequency = two_labels_otu_frequency[:, [n_otu1, n_otu2]]
print('shaped of reduced_otu_frequency: {}'.format(
reduced_otu_frequency.shape))
#print('reduced_otu_frequency:\n{}'.format(reduced_otu_frequency))
scaler = sklearn.preprocessing.StandardScaler()
# the scaler returns a copy by default
#X = scaler.fit_transform(reduced_otu_frequency)
#exit()
# the next line is pretty good
# smo.smo(reduced_otu_frequency, design_data.class_number_for_row[two_labels], 0.5)
smo.smo(reduced_otu_frequency,
design_data.class_number_for_row[two_labels], 0.5)
pl.xlabel(otu1)
pl.ylabel(otu2)
pl.gca().set_xticklabels([])
pl.gca().set_yticklabels([])
pl.show()
def svm_hmp_2_feature_plot():
print('hazzah!')
shared_file_path = '/home/jlynch/gsoc2013/data/Stool.0.03.subsample.0.03.filter.shared';
design_file_path = '/home/jlynch/gsoc2013/data/Stool.0.03.subsample.0.03.filter.mix.design';
shared_data = mothur_files.load_shared_file(shared_file_path)
design_data = mothur_files.load_design_file(design_file_path)
otu1 = 'Otu29878'
otu2 = 'Otu29552'
# where are Otu29741 and Otu29678
n_otu1 = shared_data.otu_column_names.index(otu1)
n_otu2 = shared_data.otu_column_names.index(otu2)
print('{} is on column {}'.format(otu1, n_otu1))
print('{} is on column {}'.format(otu2, n_otu2))
print('shape of design_data.class_number_for_row {}'.format(design_data.class_number_for_row.shape))
class_zero = design_data.class_number_for_row == 2.0
class_one = design_data.class_number_for_row == 1.0
print('class zero count: {}'.format(np.sum(class_zero)))
print('class one count: {}'.format(np.sum(class_one)))
two_labels = np.logical_or(class_zero, class_one)
print('shape of two_labels: {}'.format(two_labels.shape));
label_index = np.arange(design_data.class_number_for_row.shape[0])
reduced_label_index = label_index[two_labels[:,0]]
print('reduced_label_index: {}'.format(reduced_label_index))
two_labels_otu_frequency = shared_data.otu_frequency[reduced_label_index,:]
print('shape of two_labels_otu_frequency: {}'.format(two_labels_otu_frequency.shape))
reduced_otu_frequency = two_labels_otu_frequency[:,[n_otu1, n_otu2]]
print('shaped of reduced_otu_frequency: {}'.format(reduced_otu_frequency.shape))
#print('reduced_otu_frequency:\n{}'.format(reduced_otu_frequency))
scaler = sklearn.preprocessing.StandardScaler()
# the scaler returns a copy by default
#X = scaler.fit_transform(reduced_otu_frequency)
#exit()
# the next line is pretty good
# smo.smo(reduced_otu_frequency, design_data.class_number_for_row[two_labels], 0.5)
smo.smo(reduced_otu_frequency, design_data.class_number_for_row[two_labels], 0.5)
pl.xlabel(otu1)
pl.ylabel(otu2)
pl.gca().set_xticklabels([])
pl.gca().set_yticklabels([])
pl.show()
def _compute_multipliers(self, X, y):
tol=0.001
passes=4
s=smo.smo(self._c, tol, passes, X, y, self._kernel,self.path)
res=s.opt()
#print res
s.saveM(self.id)
def _compute_multipliers(self, X, y):
tol = 0.001
passes = 4
s = smo.smo(self._c, tol, passes, X, y, self._kernel, self.path)
res = s.opt()
#print res
s.saveM(self.id)
def test_smo():
print('hazzah!')
# here is some trivial data
x = np.array([[1.0, 3.0], [2.0, 5.0], [3.0, 8.0], [6.0, 4.0], [6.0, 7.0],
[7.0, 8.0], [8.0, 4.0], [3.0, 6.0]])
labels = []
labels.append('blue')
labels.append('blue')
labels.append('blue')
labels.append('blue')
labels.append('green')
labels.append('green')
labels.append('green')
labels.append('green')
smo.smo(x, labels)
def test_smo():
print('hazzah!')
# here is some trivial data
x = np.array([[1.0, 3.0],
[2.0, 5.0],
[3.0, 8.0],
[6.0, 4.0],
[6.0, 7.0],
[7.0, 8.0],
[8.0, 4.0],
[3.0, 6.0]])
labels = []
labels.append('blue')
labels.append('blue')
labels.append('blue')
labels.append('blue')
labels.append('green')
labels.append('green')
labels.append('green')
labels.append('green')
smo.smo(x, labels)