def test_classification_three_classes(self):
"""Check classification works with three classes."""
iris = load_iris()
X = iris.data
y = iris.target
clf = RVC()
clf.fit(X, y)
self.assertGreater(clf.score(X, y), 0.95)
def test_classification_two_classes(self):
"""Check classification works with two classes."""
iris = load_iris()
X = iris.data[:, 1:]
y = iris.target
# Only 2 classes needed
X = X[y != 0]
y = y[y != 0]
clf = RVC()
clf.fit(X, y)
self.assertGreater(clf.score(X, y), 0.95)
prob = clf.predict_proba(X[0, :])
p_target = np.array([[0.999, 5.538e-4]])
np.testing.assert_allclose(prob, p_target, rtol=1e-2, atol=1e-2)
feature_scaler = StandardScaler()
X_train = feature_scaler.fit_transform(X_train)
X_test = feature_scaler.transform(X_test)
####################################################################################################################
#CROSS VALIDATION SPLIT IN K-folds
######################################################################################################################
kf = KFold(n_splits=5)
kf.get_n_splits(X_train,X_test)
print(kf)
#CREATE WIDTHS FOR GRID SEARCH
width1= np.linspace(-5,4,10)
width=10**width1
#create matrix to input the scores and widths of each iteration
score_width=np.ones([len(width),2])
##################
#FIRST for loop gives the values for different widths, SECOND for loop does Kfold_validation
for i in range(len(width)):
score=0
for train_index, test_index in kf.split(X_train):
print("TRAIN:", train_index, "TEST:", test_index)
X_train1, X_test1 = X_train[train_index], X_train[test_index]
y_train1, y_test1 =y_train[train_index], y_train[test_index]
clf1=RVC(kernel='rbf',coef1=width[i])
clf1.fit(X_train1,y_train1)
score=score+clf1.score(X_test1,y_test1)
score_width[i,0]=score
score_width[i,1]=width[i]
#########################################################################################################
idx=np.argmax(score_width[:,0])
best_width=score_width[idx,1]
"""
Authors: Mrunmayee Deshpande, Lu Gan, Bruce Huang, Abhishek Venkataraman
"""
import timeit
from skrvm import RVC
import numpy as np
import os.path
import scipy.io
from import_data import import_data
## Set data path
parsed_data_path = 'parsed_data/'
[X, Y, valX, valY, testX, testY] = import_data(parsed_data_path)
scipy.io.savemat('train.mat', dict(X=X, Y=Y))
scipy.io.savemat('val.mat', dict(valX=valX, valY=valY))
scipy.io.savemat('test.mat', dict(testX=testX, testY=testY))
## Train a RVM
clf = RVC(verbose=True)
print(clf)
clf.fit(valX, valY)
clf.score(testX, testY)
New[i, :] = n.params #mdl = smt.AR(d_tot[100,:]).fit(maxlag=30, ic='aic', trend='nc') #est_order=smt.AR(d_tot[1,:]).select_order(maxlag=30, ic='aic', trend='nc') #print(est_order) #p_orders=np.zeros([1380,1]) #for i in range(1380): # X=AR(d_tot[i,:]) # n=X.fit(maxlag=4,ic='aic') # p_orders[i,0]=len(n.params) #np.mean(p_orders) #plt.scatter(New[:,1],New[:,3]) An_lab = Animal_label(tot) #shuffle data to take them in random order indx = random.sample(range(10350), 10350) a = New[indx, :] b = An_lab[indx] #define trainning and test set x_train = a[0:8000, :] y_train = b[0:8000] x_test = a[8001:, :] y_test = b[8001:] #SVM with 600 out of 690 samples as training data from skrvm import RVC clf1 = RVC(kernel='rbf') clf1.fit(x_train, y_train) clf1.score(x_test, y_test)
params = rvc_param_selection(X, Y, 5)
#
#params = rvc_param_selection2(X,Y,5)
####################TO TEST
test_err = np.zeros((15))
train_err = np.zeros((15))
coef = np.multiply(
[1000, 500, 200, 150, 100, 70, 60, 50, 40, 30, 20, 10, 5, 2, 1], 1e-6)
for i in range(15):
#coef[i] = 5e-5-(i+1)*1e-5
clf = RVC(kernel="rbf", coef1=coef[i]) # coef1: 1=46 0.1same
clf.fit(full_normPCA123_array[train_indicies],
full_subClass_array[train_indicies])
train_err[i] = clf.score(full_normPCA123_array[train_indicies],
full_subClass_array[train_indicies])
test_err[i] = clf.score(full_normPCA123_array[test_indicies],
full_subClass_array[test_indicies])
print(coef[i])
print(train_err[i])
print(test_err[i])
print("\n\n")
#####################################################################################################################
rvm_tested_coef1 = np.load("rvm_tested_coef1.npy")
rvm_tested_coef1_again = np.load("rvm_tested_coef1_again.npy")
train1 = np.load("rvm_tested_coef1_trainerr.npy")
train2 = np.load("rvm_tested_coef1_trainerr_again.npy")
test1 = np.load("rvm_tested_coef1_testerr.npy")
test2 = np.load("rvm_tested_coef1_testerr_again.npy")
#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Thu Sep 20 21:26:07 2018 @author: pan """ from skrvm import RVC from sklearn.datasets import load_iris clf = RVC() iris = load_iris() clf.fit(iris.data, iris.target) #RVC(alpha=1e-06, beta=1e-06, beta_fixed=False, bias_used=True, coef0=0.0, #coef1=None, degree=3, kernel='rbf', n_iter=3000, n_iter_posterior=50, #threshold_alpha=1000000000.0, tol=0.001, verbose=False) clf.score(iris.data, iris.target)
pd.set_option('display.notebook_repr_html', True)
import seaborn as sns
sns.set_style("whitegrid")
sns.set_context("poster")
# RVM
from skrvm import RVC
clf = RVC()
### read data from single csv file and transform to a list
dir = '/Users/weng/Downloads/'
# raw = pd.read_csv(os.path.join(dir, 'test.txt'), header=0, delimiter=';')
raw_list = pd.read_csv(os.path.join(dir, 'test.txt'), sep="\t", header=None)
# array format is needed for further processing (df -> list -> matrix(array) )
raw_X = raw_list[0].values.tolist()
raw_X = np.asarray(raw_X)
raw_y = raw_list[1].values.tolist()
raw_y = np.asarray(raw_y)
print "Read %d rows of data\n" % len(raw)
clf.fit(raw_X, raw_y)
from sklearn.datasets import load_iris
iris = load_iris()
iris.data
iris.target
clf.fit(iris.data, iris.target)
clf.score(iris.data, iris.target)
