def setupClass(cls):
cls.endog = endog = pandas.DataFrame(
np.random.random((10, 4)), columns=['y_1', 'y_2', 'y_3', 'y_4'])
exog = pandas.DataFrame(np.random.random((10, 2)),
columns=['x_1', 'x_2'])
exog.insert(0, 'const', 1)
cls.exog = exog
cls.data = sm_data.handle_data(cls.endog, cls.exog)
nrows = 10
nvars = 3
neqs = 4
cls.col_input = np.random.random(nvars)
cls.col_result = pandas.Series(cls.col_input, index=exog.columns)
cls.row_input = np.random.random(nrows)
cls.row_result = pandas.Series(cls.row_input, index=exog.index)
cls.cov_input = np.random.random((nvars, nvars))
cls.cov_result = pandas.DataFrame(cls.cov_input,
index=exog.columns,
columns=exog.columns)
cls.cov_eq_input = np.random.random((neqs, neqs))
cls.cov_eq_result = pandas.DataFrame(cls.cov_eq_input,
index=endog.columns,
columns=endog.columns)
cls.col_eq_input = np.random.random((nvars, neqs))
cls.col_eq_result = pandas.DataFrame(cls.col_eq_input,
index=exog.columns,
columns=endog.columns)
cls.xnames = ['const', 'x_1', 'x_2']
cls.ynames = ['y_1', 'y_2', 'y_3', 'y_4']
cls.row_labels = cls.exog.index
def setupClass(cls):
cls.endog = endog = pandas.DataFrame(np.random.random((10,4)),
columns=['y_1', 'y_2', 'y_3', 'y_4'])
exog = pandas.DataFrame(np.random.random((10,2)),
columns=['x_1','x_2'])
exog.insert(0, 'const', 1)
cls.exog = exog
cls.data = sm_data.handle_data(cls.endog, cls.exog)
nrows = 10
nvars = 3
neqs = 4
cls.col_input = np.random.random(nvars)
cls.col_result = pandas.Series(cls.col_input,
index=exog.columns)
cls.row_input = np.random.random(nrows)
cls.row_result = pandas.Series(cls.row_input,
index=exog.index)
cls.cov_input = np.random.random((nvars, nvars))
cls.cov_result = pandas.DataFrame(cls.cov_input,
index = exog.columns,
columns = exog.columns)
cls.cov_eq_input = np.random.random((neqs, neqs))
cls.cov_eq_result = pandas.DataFrame(cls.cov_eq_input,
index=endog.columns,
columns=endog.columns)
cls.col_eq_input = np.random.random((nvars, neqs))
cls.col_eq_result = pandas.DataFrame(cls.col_eq_input,
index=exog.columns,
columns=endog.columns)
cls.xnames = ['const', 'x_1', 'x_2']
cls.ynames = ['y_1', 'y_2', 'y_3', 'y_4']
cls.row_labels = cls.exog.index
def setupClass(cls):
super(TestArrays1dExog, cls).setupClass()
cls.endog = np.random.random(10)
exog = np.random.random(10)
cls.data = sm_data.handle_data(cls.endog, exog)
cls.exog = exog[:,None]
cls.xnames = ['x1']
cls.ynames = 'y'
def setupClass(cls):
super(TestArrays1dExog, cls).setupClass()
cls.endog = np.random.random(10)
exog = np.random.random(10)
cls.data = sm_data.handle_data(cls.endog, exog)
cls.exog = exog[:, None]
cls.xnames = ['x1']
cls.ynames = 'y'
def setupClass(cls):
super(TestStructarrays, cls).setupClass()
cls.endog = np.random.random(9).view([('y_1', 'f8')]).view(np.recarray)
exog = np.random.random(9 * 3).view([('const', 'f8'), ('x_1', 'f8'),
('x_2', 'f8')]).view(np.recarray)
exog['const'] = 1
cls.exog = exog
cls.data = sm_data.handle_data(cls.endog, cls.exog)
cls.xnames = ['const', 'x_1', 'x_2']
cls.ynames = 'y_1'
def setupClass(cls):
super(TestStructarrays, cls).setupClass()
cls.endog = np.random.random(9).view([('y_1',
'f8')]).view(np.recarray)
exog = np.random.random(9*3).view([('const', 'f8'),('x_1', 'f8'),
('x_2', 'f8')]).view(np.recarray)
exog['const'] = 1
cls.exog = exog
cls.data = sm_data.handle_data(cls.endog, cls.exog)
cls.xnames = ['const', 'x_1', 'x_2']
cls.ynames = 'y_1'
def setupClass(cls):
cls.endog = np.random.random(10)
cls.exog = np.c_[np.ones(10), np.random.random((10, 2))]
cls.data = sm_data.handle_data(cls.endog, cls.exog)
nrows = 10
nvars = 3
cls.col_result = cls.col_input = np.random.random(nvars)
cls.row_result = cls.row_input = np.random.random(nrows)
cls.cov_result = cls.cov_input = np.random.random((nvars, nvars))
cls.xnames = ['const', 'x1', 'x2']
cls.ynames = 'y'
cls.row_labels = None
def setupClass(cls):
cls.endog = np.random.random(10)
cls.exog = np.c_[np.ones(10), np.random.random((10,2))]
cls.data = sm_data.handle_data(cls.endog, cls.exog)
nrows = 10
nvars = 3
cls.col_result = cls.col_input = np.random.random(nvars)
cls.row_result = cls.row_input = np.random.random(nrows)
cls.cov_result = cls.cov_input = np.random.random((nvars, nvars))
cls.xnames = ['const', 'x1', 'x2']
cls.ynames = 'y'
cls.row_labels = None
def setupClass(cls):
cls.endog = np.random.random((10, 4))
cls.exog = np.c_[np.ones(10), np.random.random((10, 2))]
cls.data = sm_data.handle_data(cls.endog, cls.exog)
nrows = 10
nvars = 3
neqs = 4
cls.col_result = cls.col_input = np.random.random(nvars)
cls.row_result = cls.row_input = np.random.random(nrows)
cls.cov_result = cls.cov_input = np.random.random((nvars, nvars))
cls.cov_eq_result = cls.cov_eq_input = np.random.random((neqs, neqs))
cls.col_eq_result = cls.col_eq_input = np.array((neqs, nvars))
cls.xnames = ['const', 'x1', 'x2']
cls.ynames = ['y1', 'y2', 'y3', 'y4']
cls.row_labels = None
def setupClass(cls):
cls.endog = np.random.random((10,4))
cls.exog = np.c_[np.ones(10), np.random.random((10,2))]
cls.data = sm_data.handle_data(cls.endog, cls.exog)
nrows = 10
nvars = 3
neqs = 4
cls.col_result = cls.col_input = np.random.random(nvars)
cls.row_result = cls.row_input = np.random.random(nrows)
cls.cov_result = cls.cov_input = np.random.random((nvars, nvars))
cls.cov_eq_result = cls.cov_eq_input = np.random.random((neqs,neqs))
cls.col_eq_result = cls.col_eq_input = np.array((neqs, nvars))
cls.xnames = ['const', 'x1', 'x2']
cls.ynames = ['y1', 'y2', 'y3', 'y4']
cls.row_labels = None
def setupClass(cls):
cls.endog = pandas.Series(np.random.random(10), name='y_1')
exog = pandas.Series(np.random.random(10), name='x_1')
cls.exog = exog
cls.data = sm_data.handle_data(cls.endog, cls.exog)
nrows = 10
nvars = 1
cls.col_input = np.random.random(nvars)
cls.col_result = pandas.Series(cls.col_input, index=[exog.name])
cls.row_input = np.random.random(nrows)
cls.row_result = pandas.Series(cls.row_input, index=exog.index)
cls.cov_input = np.random.random((nvars, nvars))
cls.cov_result = pandas.DataFrame(cls.cov_input,
index=[exog.name],
columns=[exog.name])
cls.xnames = ['x_1']
cls.ynames = 'y_1'
cls.row_labels = cls.exog.index
def setupClass(cls):
cls.endog = pandas.DataFrame(np.random.random(10), columns=['y_1'])
exog = pandas.DataFrame(np.random.random((10, 2)),
columns=['x1', 'x2']) # names mimic defaults
exog.insert(0, 'const', 1)
cls.exog = exog.values
cls.data = sm_data.handle_data(cls.endog, cls.exog)
nrows = 10
nvars = 3
cls.col_input = np.random.random(nvars)
cls.col_result = pandas.Series(cls.col_input, index=exog.columns)
cls.row_input = np.random.random(nrows)
cls.row_result = pandas.Series(cls.row_input, index=exog.index)
cls.cov_input = np.random.random((nvars, nvars))
cls.cov_result = pandas.DataFrame(cls.cov_input,
index=exog.columns,
columns=exog.columns)
cls.xnames = ['const', 'x1', 'x2']
cls.ynames = 'y_1'
cls.row_labels = cls.endog.index
def setupClass(cls):
cls.endog = pandas.Series(np.random.random(10), name='y_1')
exog = pandas.Series(np.random.random(10), name='x_1')
cls.exog = exog
cls.data = sm_data.handle_data(cls.endog, cls.exog)
nrows = 10
nvars = 1
cls.col_input = np.random.random(nvars)
cls.col_result = pandas.Series(cls.col_input,
index = [exog.name])
cls.row_input = np.random.random(nrows)
cls.row_result = pandas.Series(cls.row_input,
index = exog.index)
cls.cov_input = np.random.random((nvars, nvars))
cls.cov_result = pandas.DataFrame(cls.cov_input,
index = [exog.name],
columns = [exog.name])
cls.xnames = ['x_1']
cls.ynames = 'y_1'
cls.row_labels = cls.exog.index
def setupClass(cls):
cls.endog = pandas.DataFrame(np.random.random(10), columns=['y_1'])
exog = pandas.DataFrame(np.random.random((10,2)),
columns=['x1','x2']) # names mimic defaults
exog.insert(0, 'const', 1)
cls.exog = exog.values
cls.data = sm_data.handle_data(cls.endog, cls.exog)
nrows = 10
nvars = 3
cls.col_input = np.random.random(nvars)
cls.col_result = pandas.Series(cls.col_input,
index=exog.columns)
cls.row_input = np.random.random(nrows)
cls.row_result = pandas.Series(cls.row_input,
index=exog.index)
cls.cov_input = np.random.random((nvars, nvars))
cls.cov_result = pandas.DataFrame(cls.cov_input,
index = exog.columns,
columns = exog.columns)
cls.xnames = ['const', 'x1', 'x2']
cls.ynames = 'y_1'
cls.row_labels = cls.endog.index
def setupClass(cls):
super(TestArrays2dEndog, cls).setupClass()
cls.endog = np.random.random((10,1))
cls.exog = np.c_[np.ones(10), np.random.random((10,2))]
cls.data = sm_data.handle_data(cls.endog, cls.exog)
def setupClass(cls):
super(TestArrays2dEndog, cls).setupClass()
cls.endog = np.random.random((10, 1))
cls.exog = np.c_[np.ones(10), np.random.random((10, 2))]
cls.data = sm_data.handle_data(cls.endog, cls.exog)
def setupClass(cls):
super(TestLists, cls).setupClass()
cls.endog = np.random.random(10).tolist()
cls.exog = np.c_[np.ones(10), np.random.random((10, 2))].tolist()
cls.data = sm_data.handle_data(cls.endog, cls.exog)
def setupClass(cls):
super(TestLists, cls).setupClass()
cls.endog = np.random.random(10).tolist()
cls.exog = np.c_[np.ones(10), np.random.random((10,2))].tolist()
cls.data = sm_data.handle_data(cls.endog, cls.exog)
