def test_data_must_have_length():
"""
Test that an error is raised when the data has no length.
"""
input_data = np.array([])
k = NaiveKDE(kernel='gaussian', bw=1)
with pytest.raises(ValueError):
k.fit(np.array(input_data))
def test_grid_must_have_length():
"""
Test that an error is raised when the grid has no length.
"""
input_data = np.array([3, 4])
k = NaiveKDE(kernel="gaussian", bw=1)
with pytest.raises(ValueError):
k.fit(np.array(input_data))
k.evaluate(np.array([]))
def test_1d_data_inputs(bw, kernel):
"""
Test that passing data as lists, tuples and NumPy arrays are all ok.
"""
input_data = [1, 2, 5, 10]
k = NaiveKDE(kernel=kernel, bw=bw)
# Arrays
k.fit(np.array(input_data))
x_1, y_1 = k.evaluate()
# Lists
k.fit(list(input_data))
x_2, y_2 = k.evaluate()
# Tuples
k.fit(tuple(input_data))
x_3, y_3 = k.evaluate()
# Arrays of shape (obs, dims)
k.fit(np.array(input_data).reshape(-1, 1))
x_4, y_4 = k.evaluate()
assert np.allclose(y_1, y_2)
assert np.allclose(y_2, y_3)
assert np.allclose(y_3, y_4)
def test_common_API_patterns():
"""
Test common API patterns.
"""
# Simplest way, with auto grid
data = [1, 2, 5, 10]
x, y = NaiveKDE().fit(data).evaluate()
# Using a pre-defined grid
x = np.linspace(-10, 50)
y1 = NaiveKDE().fit(data).evaluate(x)
# No chaining
k = NaiveKDE()
k.fit(data)
y2 = k.evaluate(x)
assert np.allclose(y1, y2)
import matplotlib.pyplot as plt
from KDEpy.NaiveKDE import NaiveKDE
# Comparing tree and naive
# -----------------------------------------
data = [3, 3.5, 4, 6, 8]
kernel = 'triweight'
bw = [3, 0.3, 1, 0.3, 2]
weights = [1, 1, 1, 1, 1]
plt.figure(figsize=(10, 4))
plt.title('Basic example of the naive KDE')
plt.subplot(1, 2, 1)
kde = NaiveKDE(kernel=kernel, bw=bw)
kde.fit(data, weights)
x = np.linspace(0, 10, num=1024)
for d, b in zip(data, bw):
k = NaiveKDE(kernel=kernel, bw=b).fit([d]).evaluate(x) / len(data)
plt.plot(x, k, color='k', ls='--')
y = kde.evaluate(x)
plt.plot(x, y)
plt.scatter(data, np.zeros_like(data))
plt.subplot(1, 2, 2)
kde = TreeKDE(kernel=kernel, bw=bw)
kde.fit(data, weights)
x = np.linspace(0, 10, num=1024)
for d, b in zip(data, bw):
k = NaiveKDE(kernel=kernel, bw=b).fit([d]).evaluate(x) / len(data)