根据所选值汇总和绘制 ndarrays 列表

Question

我有一个 ndarrays 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)

...

现在我希望整个列表为每个 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]]

....

生成 t_1 ... t_n 后，我想绘制每个 t 的类的概率，其中第一个元素表示类 (0,10,20,30)，第二个元素显示这些类出现的概率(0.1,0.7,0.15,0)。 类似于柱状图形式的直方图或概率分布，例如：

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

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

Answer 1

这是您如何使用 NumPy 计算它的方法：

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]]

作为旁注，将类值（整数）存储在浮点数组中可能不是最佳选择。 您可以考虑使用结构化数组，例如：

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)]

Answer 2

这是使用itertools.groupby的一种方法：

from statistics import mean
from itertools import groupby

def fun(t):
    s = sorted(t, key=lambda x:x[0])
    return [[k, mean(i[1] for i in v)] for k,v in groupby(s, key=lambda x: x[0])]

fun(t1)

[[0.0, 0.5],
 [10.0, 0.05],
 [20.0, 0.07500000000000001],
 [30.0, 0.07500000000000001]]

并应用于所有数组：

[fun(t) for t in [t1,t2]]

[[[0.0, 0.5],
  [10.0, 0.05],
  [20.0, 0.07500000000000001],
  [30.0, 0.07500000000000001]],
 [[0.0, 0.05], [10.0, 0.1875], [20.0, 0.07500000000000001], [30.0, 0.0]]]