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sklearn框架

函数导图

1.2. Linear and Quadratic Discriminant Analysis

import numpy as np
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])
y = np.array([1, 1, 1, 2, 2, 2])
clf = LinearDiscriminantAnalysis()
clf.fit(X, y)
print(clf.predict([[-0.8, -1]]))

Normal and Shrinkage Linear Discriminant Analysis for classification

import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import make_blobs

from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
n_train = 20  # samples for training

n_test = 200  # samples for testing

n_averages = 50  # how often to repeat classification

n_features_max = 75  # maximum number of features

step = 4  # step size for the calculation
def generate_data(n_samples, n_features):

"""Generate random blob-ish data with noisy features.
This returns an array of input data with shape (n_samples, n_features)

and an array of n_samples target labels.
Only one feature contains discriminative information, the other features

contain only noise.

"""

X, y = make_blobs(n_samples=n_samples, n_features=1, centers=[[-2], [2]])
# add non-discriminative features

if n_features > 1:

X = np.hstack([X, np.random.randn(n_samples, n_features - 1)])

return X, y
acc_clf1, acc_clf2 = [], []

n_features_range = range(1, n_features_max + 1, step)

for n_features in n_features_range:

score_clf1, score_clf2 = 0, 0

for _ in range(n_averages):

X, y = generate_data(n_train, n_features)
clf1 = LinearDiscriminantAnalysis(solver='lsqr', shrinkage='auto').fit(X, y)

clf2 = LinearDiscriminantAnalysis(solver='lsqr', shrinkage=None).fit(X, y)
X, y = generate_data(n_test, n_features)

score_clf1 += clf1.score(X, y)

score_clf2 += clf2.score(X, y)
acc_clf1.append(score_clf1 / n_averages)

acc_clf2.append(score_clf2 / n_averages)
features_samples_ratio = np.array(n_features_range) / n_train
plt.plot(features_samples_ratio, acc_clf1, linewidth=2,

label="Linear Discriminant Analysis with shrinkage", color='navy')

plt.plot(features_samples_ratio, acc_clf2, linewidth=2,

label="Linear Discriminant Analysis", color='gold')
plt.xlabel('n_features / n_samples')

plt.ylabel('Classification accuracy')
plt.legend(loc=1, prop={'size': 12})

plt.suptitle('Linear Discriminant Analysis vs. 

shrinkage Linear Discriminant Analysis (1 discriminative feature)')

plt.show()

核心函数：

		X, y = generate_data(n_train, n_features)
clf1 = LinearDiscriminantAnalysis(solver='lsqr', shrinkage='auto').fit(X, y)

clf2 = LinearDiscriminantAnalysis(solver='lsqr', shrinkage=None).fit(X, y)
X, y = generate_data(n_test, n_features)

score_clf1 += clf1.score(X, y)

score_clf2 += clf2.score(X, y)