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)