本文整理汇总了Python中sklearn.preprocessing.KBinsDiscretizer.fit方法的典型用法代码示例。如果您正苦于以下问题：Python KBinsDiscretizer.fit方法的具体用法？Python KBinsDiscretizer.fit怎么用？Python KBinsDiscretizer.fit使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类sklearn.preprocessing.KBinsDiscretizer的用法示例。
在下文中一共展示了KBinsDiscretizer.fit方法的4个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test_transform_1d_behavior
# 需要导入模块: from sklearn.preprocessing import KBinsDiscretizer [as 别名]
# 或者: from sklearn.preprocessing.KBinsDiscretizer import fit [as 别名]
def test_transform_1d_behavior():
X = np.arange(4)
est = KBinsDiscretizer(n_bins=2)
assert_raises(ValueError, est.fit, X)
est = KBinsDiscretizer(n_bins=2)
est.fit(X.reshape(-1, 1))
assert_raises(ValueError, est.transform, X)示例2: test_transform_outside_fit_range
# 需要导入模块: from sklearn.preprocessing import KBinsDiscretizer [as 别名]
# 或者: from sklearn.preprocessing.KBinsDiscretizer import fit [as 别名]
def test_transform_outside_fit_range(strategy):
X = np.array([0, 1, 2, 3])[:, None]
kbd = KBinsDiscretizer(n_bins=4, strategy=strategy, encode='ordinal')
kbd.fit(X)
X2 = np.array([-2, 5])[:, None]
X2t = kbd.transform(X2)
assert_array_equal(X2t.max(axis=0) + 1, kbd.n_bins_)
assert_array_equal(X2t.min(axis=0), [0])示例3: test_fit_transform
# 需要导入模块: from sklearn.preprocessing import KBinsDiscretizer [as 别名]
# 或者: from sklearn.preprocessing.KBinsDiscretizer import fit [as 别名]
def test_fit_transform(strategy, expected):
est = KBinsDiscretizer(n_bins=3, encode='ordinal', strategy=strategy)
est.fit(X)
assert_array_equal(expected, est.transform(X))示例4: KBinsDiscretizer
# 需要导入模块: from sklearn.preprocessing import KBinsDiscretizer [as 别名]
# 或者: from sklearn.preprocessing.KBinsDiscretizer import fit [as 别名]
xx, yy = np.meshgrid(
np.linspace(X[:, 0].min(), X[:, 0].max(), 300),
np.linspace(X[:, 1].min(), X[:, 1].max(), 300))
grid = np.c_[xx.ravel(), yy.ravel()]
ax.set_xlim(xx.min(), xx.max())
ax.set_ylim(yy.min(), yy.max())
ax.set_xticks(())
ax.set_yticks(())
i += 1
# transform the dataset with KBinsDiscretizer
for strategy in strategies:
enc = KBinsDiscretizer(n_bins=4, encode='ordinal', strategy=strategy)
enc.fit(X)
grid_encoded = enc.transform(grid)
ax = plt.subplot(len(X_list), len(strategies) + 1, i)
# horizontal stripes
horizontal = grid_encoded[:, 0].reshape(xx.shape)
ax.contourf(xx, yy, horizontal, alpha=.5)
# vertical stripes
vertical = grid_encoded[:, 1].reshape(xx.shape)
ax.contourf(xx, yy, vertical, alpha=.5)
ax.scatter(X[:, 0], X[:, 1], edgecolors='k')
ax.set_xlim(xx.min(), xx.max())
ax.set_ylim(yy.min(), yy.max())
ax.set_xticks(())