基于vsm的手写数字分类代码
时间: 2024-04-09 18:28:14 浏览: 19
Sure! Here's an example code for handwritten digit classification using Vector Space Model (VSM) in Python:
```python
import numpy as np
from sklearn.datasets import load_digits
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score
# Load the dataset
digits = load_digits()
# Split the dataset into training and test sets
X_train = digits.data[:1500]
y_train = digits.target[:1500]
X_test = digits.data[1500:]
y_test = digits.target[1500:]
# Convert the data into vectors using TF-IDF representation
vectorizer = TfidfVectorizer()
X_train = vectorizer.fit_transform(X_train.astype(str))
X_test = vectorizer.transform(X_test.astype(str))
# Train the SVM classifier
svm = SVC()
svm.fit(X_train, y_train)
# Predict the labels for test data
y_pred = svm.predict(X_test)
# Calculate the accuracy of the classifier
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)
```
In this code, we use the `load_digits` function from the `sklearn.datasets` module to load the handwritten digits dataset. Then, we split the dataset into training and test sets.
Next, we convert the data into vectors using the TF-IDF representation. The `TfidfVectorizer` from the `sklearn.feature_extraction.text` module is used for this purpose.
We then train a support vector machine (SVM) classifier using the `SVC` class from the `sklearn.svm` module. Finally, we predict the labels for the test data and calculate the accuracy of the classifier using the `accuracy_score` function from the `sklearn.metrics` module.
Note that this code assumes you have scikit-learn (`sklearn`) library installed. You can install it using `pip install scikit-learn`.
Hope this helps! Let me know if you have any more questions.
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