python - 根据所选值总结和绘制 ndarrays 列表

Question

我有一个 ndarray 列表:

list1 = [t1, t2, t3, t4, t5]

每个 t 包括:

t1 = np.array([[10,0.1],[30,0.05],[30,0.1],[20,0.1],[10,0.05],[10,0.05],[0,0.5],[20,0.05],[10,0.0]], np.float64)

t2 = np.array([[0,0.05],[0,0.05],[30,0],[10,0.25],[10,0.2],[10,0.25],[20,0.1],[20,0.05],[10,0.05]], np.float64)

...

现在我想让整个列表为每个 t 获取对应于第一个元素的值的平均值:

t1out = [[0,0.5],[10,(0.1+0.05+0.05+0)/4],[20,(0.1+0.05)/2],[30,0.075]]

t2out = [[0,0.05],[10,0.1875],[20,0.075],[30,0]]

....

在生成 t_1 ... t_n 之后，我想绘制每个 t 的类别概率，其中第一个元素代表类别 (0,10,20,30)，第二个元素显示类别的概率这些类出现 (0.1,0.7,0.15,0)。类似于直方图或条形图形式的概率分布，例如:

plt.bar([classes],[probabilities])

plt.bar([item[0] for item in t1out],[item[1] for item in t1out])

Answer 1

这是使用 NumPy 计算的方法:

import numpy as np

def mean_by_class(t, classes=None):
    # Classes should be passed if you want to ensure
    # that all classes are in the output even if they
    # are not in the current t vector
    if classes is None:
        classes = np.unique(t[:, 0])
    bins = np.r_[classes, classes[-1] + 1]
    h, _ = np.histogram(t[:, 0], bins)
    d = np.digitize(t[:, 0], bins, right=True)
    out = np.zeros(len(classes), t.dtype)
    np.add.at(out, d, t[:, 1])
    out /= h.clip(min=1)
    return np.c_[classes, out]

t1 = np.array([[10, 0.1 ], [30, 0.05], [30, 0.1 ],
               [20, 0.1 ], [10, 0.05], [10, 0.05],
               [ 0, 0.5 ], [20, 0.05], [10, 0.0 ]],
              dtype=np.float64)
print(mean_by_class(t1))
# [[ 0.     0.5  ]
#  [10.     0.05 ]
#  [20.     0.075]
#  [30.     0.075]]

附带说明一下，将类值(整数)存储在 float 组中可能不是最佳选择。您可以考虑使用 structured array相反，例如像这样:

import numpy as np

def mean_by_class(t, classes=None):
    if classes is None:
        classes = np.unique(t['class'])
    bins = np.r_[classes, classes[-1] + 1]
    h, _ = np.histogram(t['class'], bins)
    d = np.digitize(t['class'], bins, right=True)
    out = np.zeros(len(classes), t.dtype)
    out['class'] = classes
    np.add.at(out['p'], d, t['p'])
    out['p'] /= h.clip(min=1)
    return out

t1 = np.array([(10, 0.1 ), (30, 0.05), (30, 0.1 ),
               (20, 0.1 ), (10, 0.05), (10, 0.05),
               ( 0, 0.5 ), (20, 0.05), (10, 0.0 )],
              dtype=[('class', np.int32), ('p', np.float64)])
print(mean_by_class(t1))
# [( 0, 0.5  ) (10, 0.05 ) (20, 0.075) (30, 0.075)]