从噪声数据中自动检测最高峰 python
detect highest peaks automatically from noisy data python
有没有什么方法可以在不设置任何参数的情况下使用 python 库检测最高峰？。我正在开发一个用户界面，我希望算法能够自动检测最高峰...
我希望它能够检测到下图中的这些峰：
graph here
数据如下所示：
8.60291e-07
-1.5491e-06
5.64568e-07
-9.51195e-07
1.07203e-06
4.6521e-07
6.43967e-07
-9.86092e-07
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6.38977e-07
-1.93884e-06
-2.98309e-08
1.33543e-06
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-1.53549e-07
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1.59176e-06
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5.63301e-07
-8.77556e-07
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-inf
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inf
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您所描述的任务可以被视为 anomaly/outlier 检测。
一种可能的解决方案是使用 Z 分数转换，并将 z 分数高于特定阈值的每个值都视为异常值。因为没有异常值的明确定义，所以在不设置任何参数（阈值）的情况下将无法检测到此类峰值。
一个可能的解决方案是：
import numpy as np
def detect_outliers(data):
outliers = []
d_mean = np.mean(data)
d_std = np.std(data)
threshold = 3 # this defines what you would consider a peak (outlier)
for point in data:
z_score = (point - d_mean)/d_std
if np.abs(z_score) > threshold:
outliers.append(point)
return outliers
# create normal data
data = np.random.normal(size=100)
# create outliers
outliers = np.random.normal(100, size=3)
# combine normal data and outliers
full_data = data.tolist() + outliers.tolist()
# print outliers
print(detect_outliers(full_data))
如果您只想检测峰，请从代码中删除 np.abs 函数调用。
此代码片段基于 Medium Post，它还提供了另一种检测异常值的方法。
有没有什么方法可以在不设置任何参数的情况下使用 python 库检测最高峰？。我正在开发一个用户界面，我希望算法能够自动检测最高峰...
我希望它能够检测到下图中的这些峰： graph here
数据如下所示：
8.60291e-07
-1.5491e-06
5.64568e-07
-9.51195e-07
1.07203e-06
4.6521e-07
6.43967e-07
-9.86092e-07
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6.38977e-07
-1.93884e-06
-2.98309e-08
1.33543e-06
1.05064e-06
1.17332e-06
-1.53549e-07
-8.9357e-07
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3.5965e-07
1.44836e-07]
您所描述的任务可以被视为 anomaly/outlier 检测。
一种可能的解决方案是使用 Z 分数转换，并将 z 分数高于特定阈值的每个值都视为异常值。因为没有异常值的明确定义，所以在不设置任何参数（阈值）的情况下将无法检测到此类峰值。
一个可能的解决方案是：
import numpy as np
def detect_outliers(data):
outliers = []
d_mean = np.mean(data)
d_std = np.std(data)
threshold = 3 # this defines what you would consider a peak (outlier)
for point in data:
z_score = (point - d_mean)/d_std
if np.abs(z_score) > threshold:
outliers.append(point)
return outliers
# create normal data
data = np.random.normal(size=100)
# create outliers
outliers = np.random.normal(100, size=3)
# combine normal data and outliers
full_data = data.tolist() + outliers.tolist()
# print outliers
print(detect_outliers(full_data))
如果您只想检测峰，请从代码中删除 np.abs 函数调用。
此代码片段基于 Medium Post，它还提供了另一种检测异常值的方法。