• 设为首页
  • 点击收藏
  • 手机版
    手机扫一扫访问
    迪恩网络手机版
  • 关注官方公众号
    微信扫一扫关注
    迪恩网络公众号
登陆 注册

Python sparsefuncs.mean_variance_axis0函数代码示例

原作者: [db:作者] 来自: [db:来源] 收藏 邀请

本文整理汇总了Python中sklearn.utils.sparsefuncs.mean_variance_axis0函数的典型用法代码示例。如果您正苦于以下问题：Python mean_variance_axis0函数的具体用法？Python mean_variance_axis0怎么用？Python mean_variance_axis0使用的例子？那么恭喜您, 这里精选的函数代码示例或许可以为您提供帮助。



在下文中一共展示了mean_variance_axis0函数的7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，您的评价将有助于我们的系统推荐出更棒的Python代码示例。

示例1: test_mean_variance_axis0

def test_mean_variance_axis0():
    X, _ = make_classification(5, 4, random_state=0)
    # Sparsify the array a little bit
    X[0, 0] = 0
    X[2, 1] = 0
    X[4, 3] = 0
    X_lil = sp.lil_matrix(X)
    X_lil[1, 0] = 0
    X[1, 0] = 0
    X_csr = sp.csr_matrix(X_lil)

    X_means, X_vars = mean_variance_axis0(X_csr)
    assert_array_almost_equal(X_means, np.mean(X, axis=0))
    assert_array_almost_equal(X_vars, np.var(X, axis=0))

    X_csc = sp.csc_matrix(X_lil)
    X_means, X_vars = mean_variance_axis0(X_csc)

    assert_array_almost_equal(X_means, np.mean(X, axis=0))
    assert_array_almost_equal(X_vars, np.var(X, axis=0))
    assert_raises(TypeError, mean_variance_axis0, X_lil)

    X = X.astype(np.float32)
    X_csr = X_csr.astype(np.float32)
    X_csc = X_csr.astype(np.float32)
    X_means, X_vars = mean_variance_axis0(X_csr)
    assert_array_almost_equal(X_means, np.mean(X, axis=0))
    assert_array_almost_equal(X_vars, np.var(X, axis=0))
    X_means, X_vars = mean_variance_axis0(X_csc)
    assert_array_almost_equal(X_means, np.mean(X, axis=0))
    assert_array_almost_equal(X_vars, np.var(X, axis=0))
    assert_raises(TypeError, mean_variance_axis0, X_lil)
开发者ID:1oscar，项目名称:scikit-learn，代码行数:32，代码来源:test_sparsefuncs.py


示例2: test_scale_function_without_centering

def test_scale_function_without_centering():
    rng = np.random.RandomState(42)
    X = rng.randn(4, 5)
    X[:, 0] = 0.0  # first feature is always of zero
    X_csr = sparse.csr_matrix(X)

    X_scaled = scale(X, with_mean=False)
    assert_false(np.any(np.isnan(X_scaled)))

    X_csr_scaled = scale(X_csr, with_mean=False)
    assert_false(np.any(np.isnan(X_csr_scaled.data)))

    # test csc has same outcome
    X_csc_scaled = scale(X_csr.tocsc(), with_mean=False)
    assert_array_almost_equal(X_scaled, X_csc_scaled.toarray())

    # raises value error on axis != 0
    assert_raises(ValueError, scale, X_csr, with_mean=False, axis=1)

    assert_array_almost_equal(X_scaled.mean(axis=0),
                              [0., -0.01, 2.24, -0.35, -0.78], 2)
    assert_array_almost_equal(X_scaled.std(axis=0), [0., 1., 1., 1., 1.])
    # Check that X has not been copied
    assert_true(X_scaled is not X)

    X_csr_scaled_mean, X_csr_scaled_std = mean_variance_axis0(X_csr_scaled)
    assert_array_almost_equal(X_csr_scaled_mean, X_scaled.mean(axis=0))
    assert_array_almost_equal(X_csr_scaled_std, X_scaled.std(axis=0))
开发者ID:CodeGenerator，项目名称:scikit-learn，代码行数:28，代码来源:test_data.py


示例3: test_scaler_without_centering

def test_scaler_without_centering():
    rng = np.random.RandomState(42)
    X = rng.randn(4, 5)
    X[:, 0] = 0.0  # first feature is always of zero
    X_csr = sparse.csr_matrix(X)
    X_csc = sparse.csc_matrix(X)

    assert_raises(ValueError, StandardScaler().fit, X_csr)

    null_transform = StandardScaler(with_mean=False, with_std=False, copy=True)
    X_null = null_transform.fit_transform(X_csr)
    assert_array_equal(X_null.data, X_csr.data)
    X_orig = null_transform.inverse_transform(X_null)
    assert_array_equal(X_orig.data, X_csr.data)

    scaler = StandardScaler(with_mean=False).fit(X)
    X_scaled = scaler.transform(X, copy=True)
    assert_false(np.any(np.isnan(X_scaled)))

    scaler_csr = StandardScaler(with_mean=False).fit(X_csr)
    X_csr_scaled = scaler_csr.transform(X_csr, copy=True)
    assert_false(np.any(np.isnan(X_csr_scaled.data)))

    scaler_csc = StandardScaler(with_mean=False).fit(X_csc)
    X_csc_scaled = scaler_csr.transform(X_csc, copy=True)
    assert_false(np.any(np.isnan(X_csc_scaled.data)))

    assert_equal(scaler.mean_, scaler_csr.mean_)
    assert_array_almost_equal(scaler.std_, scaler_csr.std_)

    assert_equal(scaler.mean_, scaler_csc.mean_)
    assert_array_almost_equal(scaler.std_, scaler_csc.std_)

    assert_array_almost_equal(
        X_scaled.mean(axis=0), [0., -0.01, 2.24, -0.35, -0.78], 2)
    assert_array_almost_equal(X_scaled.std(axis=0), [0., 1., 1., 1., 1.])

    X_csr_scaled_mean, X_csr_scaled_std = mean_variance_axis0(X_csr_scaled)
    assert_array_almost_equal(X_csr_scaled_mean, X_scaled.mean(axis=0))
    assert_array_almost_equal(X_csr_scaled_std, X_scaled.std(axis=0))

    # Check that X has not been modified (copy)
    assert_true(X_scaled is not X)
    assert_true(X_csr_scaled is not X_csr)

    X_scaled_back = scaler.inverse_transform(X_scaled)
    assert_true(X_scaled_back is not X)
    assert_true(X_scaled_back is not X_scaled)
    assert_array_almost_equal(X_scaled_back, X)

    X_csr_scaled_back = scaler_csr.inverse_transform(X_csr_scaled)
    assert_true(X_csr_scaled_back is not X_csr)
    assert_true(X_csr_scaled_back is not X_csr_scaled)
    assert_array_almost_equal(X_csr_scaled_back.toarray(), X)

    X_csc_scaled_back = scaler_csr.inverse_transform(X_csc_scaled.tocsc())
    assert_true(X_csc_scaled_back is not X_csc)
    assert_true(X_csc_scaled_back is not X_csc_scaled)
    assert_array_almost_equal(X_csc_scaled_back.toarray(), X)
开发者ID:CodeGenerator，项目名称:scikit-learn，代码行数:59，代码来源:test_data.py


示例4: test_mean_variance_axis0

def test_mean_variance_axis0():
    X, _ = make_classification(5, 4, random_state=0)
    # Sparsify the array a little bit
    X[0, 0] = 0
    X[2, 1] = 0
    X[4, 3] = 0
    X_csr = sp.csr_matrix(X)
    X_csr[1, 0] = 0
    X[1, 0] = 0
    X_means, X_vars = mean_variance_axis0(X_csr)
    assert_array_almost_equal(X_means, np.mean(X, axis=0))
    assert_array_almost_equal(X_vars, np.var(X, axis=0))
    
    X_csc = sp.csc_matrix(X)
    X_csc[1, 0] = 0
    X[1, 0] = 0
    X_means, X_vars = mean_variance_axis0(X_csc)

    assert_array_almost_equal(X_means, np.mean(X, axis=0))
    assert_array_almost_equal(X_vars, np.var(X, axis=0))
开发者ID:edmoody，项目名称:scikit-learn，代码行数:20，代码来源:test_sparsefuncs.py


示例5: test_scale_function_without_centering

def test_scale_function_without_centering():
    rng = np.random.RandomState(42)
    X = rng.randn(4, 5)
    X[:, 0] = 0.0  # first feature is always of zero
    X_csr = sp.csr_matrix(X)

    X_scaled = scale(X, with_mean=False)
    assert_false(np.any(np.isnan(X_scaled)))

    X_csr_scaled = scale(X_csr, with_mean=False)
    assert_false(np.any(np.isnan(X_csr_scaled.data)))

    assert_array_almost_equal(X_scaled.mean(axis=0), [0.0, -0.01, 2.24, -0.35, -0.78], 2)
    assert_array_almost_equal(X_scaled.std(axis=0), [0.0, 1.0, 1.0, 1.0, 1.0])
    # Check that X has not been copied
    assert_true(X_scaled is not X)

    X_csr_scaled_mean, X_csr_scaled_std = mean_variance_axis0(X_csr_scaled)
    assert_array_almost_equal(X_csr_scaled_mean, X_scaled.mean(axis=0))
    assert_array_almost_equal(X_csr_scaled_std, X_scaled.std(axis=0))
开发者ID:kalaidin，项目名称:scikit-learn，代码行数:20，代码来源:test_preprocessing.py


示例6: test_scaler_without_centering

def test_scaler_without_centering():
    rng = np.random.RandomState(42)
    X = rng.randn(4, 5)
    X[:, 0] = 0.0  # first feature is always of zero
    X_csr = sp.csr_matrix(X)

    scaler = Scaler(with_mean=False).fit(X)
    X_scaled = scaler.transform(X, copy=True)
    assert_false(np.any(np.isnan(X_scaled)))

    scaler_csr = Scaler(with_mean=False).fit(X_csr)
    X_csr_scaled = scaler_csr.transform(X_csr, copy=True)
    assert_false(np.any(np.isnan(X_csr_scaled.data)))

    assert_equal(scaler.mean_, scaler_csr.mean_)
    assert_array_almost_equal(scaler.std_, scaler_csr.std_)

    assert_array_almost_equal(
        X_scaled.mean(axis=0), [0., -0.01,  2.24, -0.35, -0.78], 2)
    assert_array_almost_equal(X_scaled.std(axis=0), [0., 1., 1., 1., 1.])

    X_csr_scaled_mean, X_csr_scaled_std = mean_variance_axis0(X_csr_scaled)
    assert_array_almost_equal(X_csr_scaled_mean, X_scaled.mean(axis=0))
    assert_array_almost_equal(X_csr_scaled_std, X_scaled.std(axis=0))

    # Check that X has not been modified (copy)
    assert_true(X_scaled is not X)
    assert_true(X_csr_scaled is not X_csr)

    X_scaled_back = scaler.inverse_transform(X_scaled)
    assert_true(X_scaled_back is not X)
    assert_true(X_scaled_back is not X_scaled)
    assert_array_almost_equal(X_scaled_back, X)

    X_csr_scaled_back = scaler_csr.inverse_transform(X_csr_scaled)
    assert_true(X_csr_scaled_back is not X_csr)
    assert_true(X_csr_scaled_back is not X_csr_scaled)
    assert_array_almost_equal(X_scaled_back, X)
开发者ID:AlexLerman，项目名称:scikit-learn，代码行数:38，代码来源:test_preprocessing.py


示例7: test_scaler_int

def test_scaler_int():
    # test that scaler converts integer input to floating
    # for both sparse and dense matrices
    rng = np.random.RandomState(42)
    X = rng.randint(20, size=(4, 5))
    X[:, 0] = 0  # first feature is always of zero
    X_csr = sparse.csr_matrix(X)
    X_csc = sparse.csc_matrix(X)

    null_transform = StandardScaler(with_mean=False, with_std=False, copy=True)
    with warnings.catch_warnings(record=True):
        X_null = null_transform.fit_transform(X_csr)
    assert_array_equal(X_null.data, X_csr.data)
    X_orig = null_transform.inverse_transform(X_null)
    assert_array_equal(X_orig.data, X_csr.data)

    with warnings.catch_warnings(record=True):
        scaler = StandardScaler(with_mean=False).fit(X)
        X_scaled = scaler.transform(X, copy=True)
    assert_false(np.any(np.isnan(X_scaled)))

    with warnings.catch_warnings(record=True):
        scaler_csr = StandardScaler(with_mean=False).fit(X_csr)
        X_csr_scaled = scaler_csr.transform(X_csr, copy=True)
    assert_false(np.any(np.isnan(X_csr_scaled.data)))

    with warnings.catch_warnings(record=True):
        scaler_csc = StandardScaler(with_mean=False).fit(X_csc)
        X_csc_scaled = scaler_csr.transform(X_csc, copy=True)
    assert_false(np.any(np.isnan(X_csc_scaled.data)))

    assert_equal(scaler.mean_, scaler_csr.mean_)
    assert_array_almost_equal(scaler.std_, scaler_csr.std_)

    assert_equal(scaler.mean_, scaler_csc.mean_)
    assert_array_almost_equal(scaler.std_, scaler_csc.std_)

    assert_array_almost_equal(
        X_scaled.mean(axis=0),
        [0., 1.109, 1.856, 21., 1.559], 2)
    assert_array_almost_equal(X_scaled.std(axis=0), [0., 1., 1., 1., 1.])

    X_csr_scaled_mean, X_csr_scaled_std = mean_variance_axis0(
        X_csr_scaled.astype(np.float))
    assert_array_almost_equal(X_csr_scaled_mean, X_scaled.mean(axis=0))
    assert_array_almost_equal(X_csr_scaled_std, X_scaled.std(axis=0))

    # Check that X has not been modified (copy)
    assert_true(X_scaled is not X)
    assert_true(X_csr_scaled is not X_csr)

    X_scaled_back = scaler.inverse_transform(X_scaled)
    assert_true(X_scaled_back is not X)
    assert_true(X_scaled_back is not X_scaled)
    assert_array_almost_equal(X_scaled_back, X)

    X_csr_scaled_back = scaler_csr.inverse_transform(X_csr_scaled)
    assert_true(X_csr_scaled_back is not X_csr)
    assert_true(X_csr_scaled_back is not X_csr_scaled)
    assert_array_almost_equal(X_csr_scaled_back.toarray(), X)

    X_csc_scaled_back = scaler_csr.inverse_transform(X_csc_scaled.tocsc())
    assert_true(X_csc_scaled_back is not X_csc)
    assert_true(X_csc_scaled_back is not X_csc_scaled)
    assert_array_almost_equal(X_csc_scaled_back.toarray(), X)
开发者ID:CodeGenerator，项目名称:scikit-learn，代码行数:65，代码来源:test_data.py



注：本文中的sklearn.utils.sparsefuncs.mean_variance_axis0函数示例由纯净天空整理自Github/MSDocs等源码及文档管理平台，相关代码片段筛选自各路编程大神贡献的开源项目，源码版权归原作者所有，传播和使用请参考对应项目的License；未经允许，请勿转载。

鲜花
握手
雷人
路过
鸡蛋
该文章已有0人参与评论

请发表评论

全部评论

专题导读
More+
上一篇：
Python testing.all_estimators函数代码示例发布时间：2022-05-27
下一篇：
Python sparsefuncs.mean_variance_axis函数代码示例发布时间：2022-05-27
热门推荐
More+
热门话题
More+
阅读排行榜

扫描微信二维码

查看手机版网站

随时了解更新最新资讯

139-2527-9053

在线客服（服务时间 9:00～18:00）

在线QQ客服
地址：深圳市南山区西丽大学城创智工业园
电邮：jeky_zhao#qq.com
移动电话：139-2527-9053

Powered by 互联科技 X3.4© 2001-2213 极客世界.|Sitemap