One-dimensional scatter plot features are extracted and a classifier is designed

To extract the features of a one-dimensional scatter plot, you can try the following methods:

Statistical characteristics: For a one-dimensional scatter plot, you can calculate statistical characteristics such as mean, variance, standard deviation, skewness, and kurtosis. These characteristics can well reflect the distribution and morphological characteristics of data.

Fourier Transform characteristics: If your data is periodic, you can use the Fourier transform to convert it into frequency-domain data and extract its frequency-domain characteristics. For example, you can calculate the peak, center frequency, bandwidth, and other characteristics of the spectrum.

Wavelet transform features: For aperiodic data, you can use wavelet transform to convert it into time-frequency domain data and extract its time-frequency domain features. For example, you can calculate features such as the energy, entropy, average value, and so on of a wavelet packet.

For raw spectral data, you can consider the following processing methods to facilitate feature extraction:

Remove baseline drift: Since spectral data is often affected by baseline drift, you can baseline correct the data to facilitate more accurate extraction of its features.

Data standardization: In order to eliminate dimensional influence between data, you can standardize data, such as normalization according to its mean and standard deviation.

Spectral resampling: If the resolution of your spectral data is too high, you may consider resampling it in order to reduce the dimension of the data and reduce the complexity of feature extraction and classification.

Spectral smoothing: In order to reduce the impact of data noise on feature extraction, you can smooth the spectral data, such as using average filtering or median filtering.

Finally, a classifier can be trained using machine learning algorithms to easily distinguish between different solutions. You can use the extracted features as input to the classifier, and train and test with some samples of known classifications. Commonly used classifiers include support vector machine, random forest, neural network and so on.

Refer to GPT and own ideas, for the feature extraction of one-dimensional scatter graph, commonly used methods include:

Peak characteristics: Find the location, height, width and other characteristics of the peak.

Curve shape features: including curve slope, inflection point position and other features.

Statistical characteristics: including mean value, standard deviation, skewness, kurtosis and other characteristics.

Frequency domain features: Fourier transform and other methods are used to convert signals to frequency domain, and extract frequency domain features.

Here is a simple example code to extract the statistical characteristics of a one-dimensional scatter plot. Suppose the data is already stored in a one-dimensional array of data:

import numpy as np

# 计算均值、标准差、偏度和峰度
mean = np.mean(data)
std = np.std(data)
skewness = np.mean((data - mean) ** 3) / (std ** 3)
kurtosis = np.mean((data - mean) ** 4) / (std ** 4) - 3

# 输出结果
print("均值：", mean)
print("标准差：", std)
print("偏度：", skewness)
print("峰度：", kurtosis)

For processing raw spectral data, common methods include:

Baseline correction: Eliminate the baseline disturbance in the spectrum, making the signal more stable.

Denoising: remove the noise in the signal and make the signal cleaner.

Data preprocessing: operations such as normalization, smoothing and dimensionality reduction can improve the reliability of features and the accuracy of classifiers.

Feature extraction: Transform the original data into a more representative feature vector for the classifier to distinguish.

Here is a simple example code for baseline correction and normalization of raw spectral data. Assume that the data has been stored in a two-dimensional array of data, with each row representing a sample and each column representing a wavelength:

import numpy as np
from scipy.signal import savgol_filter

# 对每个样本进行基线校正
baseline = np.min(data, axis=1)
data = data - baseline.reshape(-1, 1)

# 对每个样本进行归一化
max_value = np.max(data, axis=1)
data = data / max_value.reshape(-1, 1)

# 平滑处理
data = savgol_filter(data, 21, 3, axis=1)

# 输出结果
print("处理后的数据：", data)

It is important to note that this is a simple example code, and the actual application may require more complex processing and feature extraction according to the characteristics of the data.

To extract the features of a one-dimensional scatter plot and design a classifier, you can use the Scikit-learn library in Python.

Here is an example code, assuming you have a CSV file that stores the data for the scatter plot, and the first column is the category label(0 for table sugar, 1 for table salt solution).

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score

# 读取 CSV 文件，存储为 Pandas DataFrame 对象
df = pd.read_csv('scatterplot_data.csv')

# 提取特征和标签
X = df.drop(['label'], axis=1)
y = df['label']

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 训练逻辑回归模型
model = LogisticRegression()
model.fit(X_train, y_train)

# 在测试集上进行预测
y_pred = model.predict(X_test)

# 计算准确率
accuracy = accuracy_score(y_test, y_pred)
print('Accuracy:', accuracy)

If you have more than one feature, you can use other classification algorithms, such as support vector machines(SVM) or decision tree classifiers, to train the model and make classification predictions. Before training the model, you can use feature selection methods to select the features with the most predictive power to improve the performance of the classifier.

The little girl referred to bing and GPT to write part of the content :
to extract one-dimensional scatter plot features and design the classifier, requiring sugar solution and edible oil solution as examples, Feature extraction and classification of resonance wavelength distribution were carried out.

First, we need to extract features from the resonance wavelength profile. A variety of methods can be used, such as calculating peak value, peak width, maximum value, minimum value, average value, standard difference, etc. These characteristics can be used to characterize the spectral properties of different solutions.

Then, we need to use these features to design the classifier. Here, machine learning methods can be used, such as KNN, SVM, decision tree, etc. These methods can effectively classify the spectral data of different solutions, so as to realize the distinction of different solutions.

Finally, experiments are needed to verify the performance of the designed classifier. We can use accuracy, recall rate, F1-score and other indicators to evaluate the performance of the designed classifier and optimize it.
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