資源簡介
機器學習基礎教程(Rogers)內的源碼,包含.m和.r文件,大家下載學習吧!
代碼片段和文件信息
import?numpy?as?np
class?SimpleSVM(object):
def?__init__(selftrainxtraintkernel=‘rbf‘kpar?=?1.0C?=?2tol=1e-10max_passes?=?1000):
self.trainx?=?trainx
self.traint?=?traint
self.kernel?=?kernel
self.kpar?=?kpar
self.C?=?C
self.tol?=?tol
self.max_passes?=?max_passes
self.N?=?len(self.traint)
self.K?=?self.kernel_matrix()
self.alpha?=?np.zeros_like(traint)
self.b?=?0.0
def?test_kernel(selftestx):
testK?=?np.zeros_like(self.traint)
if?self.kernel?==?‘linear‘:
testK?=?np.dot(self.trainxtestx.T)[:None]
elif?self.kernel?==?‘rbf‘:
for?i?in?range(self.N):
testK[i]?=?np.exp(-self.kpar?*?((self.trainx[i:]-testx)**2).sum())
return?testK
def?kernel_matrix(self):
K?=?None
if?self.kernel?==?‘linear‘:
K?=?np.dot(self.trainxself.trainx.T)
elif?self.kernel?==?‘rbf‘:
K?=?np.zeros((self.Nself.N))
for?i?in?range(self.N):
for?j?in?range(self.N):
K[ij]?=?np.exp(-self.kpar?*?((self.trainx[i:]-self.trainx[j:])**2).sum())
return?K
def?train_predict(selfi):
ksub?=?self.K[i:][:None]
return?(ksub*self.alpha*self.traint).sum()?+?self.b
def?test_predict(selftestx):
testK?=?self.test_kernel(testx)
return?(testK*self.alpha*self.traint).sum()?+?self.b
def?smo_optimise(self):
????#?initialise
????self.alpha?=?np.zeros_like(self.traint)
????self.b?=?0
????passes?=?0
????while?passes? ????????num_changed_alphas?=?0
????????for?i?in?range(self.N):
????????????Ei?=?self.train_predict(i)?-?self.traint[i]
????????????if?(self.traint[i]*Ei?-self.tol?and?self.alpha[i]??self.tol?and?self.alpha[i]?>?0):
????????????????j?=?np.random.randint(self.N)
????????????????Ej?=?self.train_predict(j)?-?self.traint[j]
????????????????alphai?=?float(self.alpha[i])
????????????????alphaj?=?float(self.alpha[j])
????????????????if?self.traint[i]?==?self.traint[j]:
????????????????????L?=?max((0alphai+alphaj-self.C))
????????????????????H?=?min((self.Calphai+alphaj))
????????????????else:
????????????????????L?=?max((0alphaj-alphai))
????????????????????H?=?min((self.Cself.C+alphaj-alphai))
????????????????if?L==H:
????????????????????continue
????????????????eta?=?2*self.K[ij]?-?self.K[ii]?-?self.K[jj]
????????????????if?eta?>=?0:
????????????????????continue
????????????????self.alpha[j]?=?alphaj?-?(self.traint[j]*(Ei-Ej))/eta
????????????????if?self.alpha[j]?>?H:
????????????????????self.alpha[j]?=?H
????????????????if?self.alpha[j]? ????????????????????self.alpha[j]?=?L
????????????????if?abs(self.alpha[j]-alphaj)?1e-5:
????????????????????continue
????????????????self.alpha[i]?=?self.alpha[i]?+?self.traint[i]*self.traint[j]*(alphaj?-?self.alpha[j])
????????????????
????????????????b1?=?self.b?-?Ei?-?self.traint[i]*(self.alpha[i]?-?alphai)*self.K[ii]?-?self.traint[j]*(self.alpha[j]?-?alphaj)*self.K[ij]
????????????????b2?=?self.b?-?Ej?-?self.traint[i]*(self.alpha[i]?-?alphai)*self.K[ij]?-?s
?屬性????????????大小?????日期????時間???名稱
-----------?---------??----------?-----??----
????.......?????64956??2018-10-18?04:38??機器學習基礎教程源碼\labs\bayesian_regression\.ipynb_checkpoints\Bayesian?Regression?for?the?Olympic?Data-checkpoint.ipynb
????.......????189788??2018-10-18?04:38??機器學習基礎教程源碼\labs\bayesian_regression\bayes_regression2.pdf
????.......???????274??2018-10-18?04:38??機器學習基礎教程源碼\labs\bayesian_regression\data100m.csv
????.......????208218??2018-10-18?04:38??機器學習基礎教程源碼\labs\bayesian_regression\olymp_bayes.pdf
????.......?????27957??2018-10-18?04:38??機器學習基礎教程源碼\labs\classification\.ipynb_checkpoints\KNN-checkpoint.ipynb
????.......????160746??2018-10-18?04:38??機器學習基礎教程源碼\labs\classification\knn.pdf
????.......????163994??2018-10-18?04:38??機器學習基礎教程源碼\labs\classification\logreg.pdf
????.......????170770??2018-10-18?04:38??機器學習基礎教程源碼\labs\classification\logreg_metropolis_skeleton.ipynb
????.......????209660??2018-10-18?04:38??機器學習基礎教程源碼\labs\classification\naivebayes.pdf
????.......??????3466??2018-10-18?04:38??機器學習基礎教程源碼\labs\classification\simplesvm.py
????.......????122629??2018-10-18?04:38??機器學習基礎教程源碼\labs\classification\SVM.ipynb
????.......????118909??2018-10-18?04:38??機器學習基礎教程源碼\labs\classification\svm.pdf
????.......?????21414??2018-10-18?04:38??機器學習基礎教程源碼\labs\classification\testx.csv
????.......??????5357??2018-10-18?04:38??機器學習基礎教程源碼\labs\classification\trainx.csv
????.......????985610??2018-10-18?04:38??機器學習基礎教程源碼\labs\clustering\Gaussian_mixture.ipynb
????.......????146526??2018-10-18?04:38??機器學習基礎教程源碼\labs\clustering\kmeans.pdf
????.......????101392??2018-10-18?04:38??機器學習基礎教程源碼\labs\clustering\K_means_skeleton.ipynb
????.......???????274??2018-10-18?04:38??機器學習基礎教程源碼\labs\linear_regression\data100m.csv
????.......?????42071??2018-10-18?04:38??機器學習基礎教程源碼\labs\linear_regression\linear_regression.ipynb
????.......????144702??2018-10-18?04:38??機器學習基礎教程源碼\labs\linear_regression\linear_regression.pdf
????.......??????2255??2018-10-18?04:38??機器學習基礎教程源碼\labs\linear_regression\max_like.mk
????.......????178518??2018-10-18?04:38??機器學習基礎教程源碼\labs\linear_regression\max_like.pdf
????.......??????2612??2018-10-18?04:38??機器學習基礎教程源碼\labs\linear_regression\vector_matrices_cv.mk
????.......????139534??2018-10-18?04:38??機器學習基礎教程源碼\labs\linear_regression\vector_matrices_cv.pdf
????.......??????1792??2018-10-18?04:38??機器學習基礎教程源碼\matlab\chapter1\.svn\entries
????.......??????2140??2018-10-18?04:38??機器學習基礎教程源碼\matlab\chapter1\.svn\text-ba
????.......??????2140??2018-10-18?04:38??機器學習基礎教程源碼\matlab\chapter1\.svn\text-ba
????.......???????852??2018-10-18?04:38??機器學習基礎教程源碼\matlab\chapter1\.svn\text-ba
????.......??????1087??2018-10-18?04:38??機器學習基礎教程源碼\matlab\chapter1\.svn\text-ba
????.......??????1021??2018-10-18?04:38??機器學習基礎教程源碼\matlab\chapter1\.svn\text-ba
............此處省略1539個文件信息
- 上一篇:大麥DW22D原廠編程器固件
- 下一篇:銀行業數據治理實踐德勤.pdf
評論
共有 條評論