lasseregin / svm-w-smo Goto Github PK
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License: MIT License
Simple implementation of a Support Vector Machine using the Sequential Minimal Optimization (SMO) algorithm for training.
License: MIT License
Hey Lasse,
The calc_w function in your SVM function seems wrong, it should return a vector but it ends up with a matrix. I rewrote the calc_w as follow, it worked, let me know what you think.
def calc_w(self, alpha, y, X):
X_t = X.transpose()
X_ta = np.matmul(X_t, X)
X_tai = np.linalg.inv(X_ta)
ols = np.matmul(X_tai, X_t)
ols = np.matmul(ols, y)
return ols
Hey Lasse,
When I tried your SVM, it seems calc_w was not working properly as alpha*y
calculated the dot product of these two vectors so that your function doesn't really return the right w
. I replace your function with mine below, which works. Let me know what you think, I can do a pull request to correct it.
def calc_w(self, alpha, y, X):
new = np.multiply(alpha,y)
return np.dot(X.T, new)
Yitao
Hello! Excuse me.
I think this kernel method quadratic
have error.
This is my test code.
import warnings
warnings.filterwarnings('ignore')
import numpy as np
import random
# load yourself model
from SVM import SVM
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score
from sklearn.datasets.samples_generator import make_blobs
from sklearn.model_selection import train_test_split
def test_SVM():
i = 1
np.random.seed(12345)
while True:
# generate dataset
X, Y = make_blobs(
n_samples=np.random.randint(2, 100),
n_features=np.random.randint(2, 100),
centers=2, random_state=i,
)
X, X_test, Y, Y_test = train_test_split(X, Y, test_size=0.3, random_state=i)
# ignore split error(train/test data only 1 class)
if 0 not in Y or 1 not in Y:
continue
# generate param
C = random.uniform(0.1, 0.9)
max_iter = random.uniform(50, 500)
kernel = "quadratic"
tol = random.uniform(0.000001, 0.1)
# fit and predict
clf = SVM(C=C, max_iter=max_iter, kernel=kernel, tol=tol)
clf.fit(X, Y)
pred = clf.predict(X_test)
print(accuracy_score(Y_test, pred))
if __name__ == "__main__":
test_SVM()
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