def setupClass(cls):
cls.decimal_density = 2 # low accuracy because binning is different
res1 = KDE(Xi)
res1.fit(kernel="gau", fft=True, bw="silverman")
cls.res1 = res1
rfname2 = os.path.join(curdir,'results','results_kde_fft.csv')
cls.res_density = np.genfromtxt(open(rfname2, 'rb'))
def setupClass(cls):
cls.decimal_density = 2 # low accuracy because binning is different
res1 = KDE(Xi)
res1.fit(kernel="gau", fft=True, bw="silverman")
cls.res1 = res1
rfname2 = os.path.join(curdir, 'results', 'results_kde_fft.csv')
cls.res_density = np.genfromtxt(open(rfname2, 'rb'))
def setupClass(cls):
res1 = KDE(Xi)
weights = np.linspace(1,100,200)
res1.fit(kernel="gau", gridsize=50, weights=weights, fft=False,
bw="silverman")
cls.res1 = res1
rfname = os.path.join(curdir,'results','results_kde_weights.csv')
cls.res_density = np.genfromtxt(open(rfname, 'rb'), skip_header=1)
def setupClass(cls):
res1 = KDE(Xi)
weights = np.linspace(1, 100, 200)
res1.fit(kernel="gau",
gridsize=50,
weights=weights,
fft=False,
bw="silverman")
cls.res1 = res1
rfname = os.path.join(curdir, 'results', 'results_kde_weights.csv')
cls.res_density = np.genfromtxt(open(rfname, 'rb'), skip_header=1)
def setupClass(cls):
res1 = KDE(Xi)
res1.fit(kernel="biw", fft=False, bw="silverman")
cls.res1 = res1
cls.res_density = KDEResults["biw_d"]
def setupClass(cls):
res1 = KDE(Xi)
res1.fit(kernel="biw", fft=False, bw="silverman")
cls.res1 = res1
cls.res_density = KDEResults["biw_d"]