olinguyen

// Keybad gun bindings
bind "KP_INS" "buy defuser"
bind "KP_END" "buy ak47; buy m4a1;"
bind "KP_DOWNARROW" "buy galil; buy famas"
bind "KP_PGDN" "buy awp"
bind "KP_LEFTARROW" "buy p250"
bind "KP_5" "buy p90"
bind "KP_RIGHTARROW" "buy mp7"
bind "KP_HOME" "buy deagle"
bind "KP_UPARROW" "buy vest"

olinguyen

"""
The following code crashes with this error message:

---> 15 result = cv.evaluate()

SystemError: [ERROR] In file /build/shogun-v9ad6W/shogun-6.0.0+1SNAPSHOT201704270057/src/shogun/labels/Labels.cpp 
line 67: assertion m_current_values.vector && idx < get_num_labels() failed in 
virtual float64_t shogun::CLabels::get_value(int32_t) 
file /build/shogun-v9ad6W/shogun-6.0.0+1SNAPSHOT201704270057/src/shogun/labels/Labels.cpp line 67
"""

olinguyen

import numpy as np
from modshogun import *

roc = ROCEvaluation()
roc.evaluate(y_pred, y_test)

auc = roc.get_auROC()
print(auc) # 0.845606993532

roc = ROCEvaluation()

olinguyen

import numpy as np
from modshogun import *

features_train = RealFeatures(np.random.random_sample((10, 5)))
features_test = RealFeatures(np.random.random_sample((10, 5)))
labels_train = MulticlassLabels(np.array([np.random.randint(3) for i in range(5)]).astype(np.float))
labels_test = MulticlassLabels(np.array([np.random.randint(3) for i in range(5)]).astype(np.float))

m_vote = MajorityVote()

olinguyen

import numpy as np
from modshogun import MulticlassLabels

int_labels = np.array([0, 1, 2], dtype=np.int)

MulticlassLabels(int_labels)
MulticlassLabels(int_labels.astype(np.int32))

olinguyen

import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm, datasets

# import some data to play with
iris = datasets.load_iris()

num_samples = len(iris.data)

np.random.seed(seed=42)

olinguyen

import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm, datasets
from sklearn.ensemble import RandomForestClassifier
from modshogun import *

#np.random.seed(42)

X = np.array([[-1.0, -1.0], [-1.2, -1.4], [-3.4, -2.2], [1.1, 1.2]])
y = np.array([0, 0, 1, 1]).astype(np.float)

olinguyen

# Generates data for y = (x1 v x2) > 5 

import numpy as np
from sklearn.ensemble import RandomForestClassifier

X1 = np.random.randint(5, 10, size=(5, 2))
X2 = np.random.randint(0, 5, size=(5, 2))

X = np.concatenate((X1, X2))
y = [1.0 if i > 5 else 0.0 for i in range(10)]

olinguyen

GSoC 2017 Final Report: Data Project - Patient Monitoring and Decision Support using Health Data

Name: Olivier Nguyen

Mentors: Lea Goetz, Heiko Strathmann

Organization: Shogun Machine Learning Toolbox

Abstract