if mean < minMean:
minMean = mean
if mean > maxMean:
maxMean = mean
if std > maxStd:
maxStd = std
l = 2*(minMean - maxStd)
r = 2*(maxMean + maxStd)
for m, s in toPlot:
normDist(m,s,l,r)
formt = FormatJson(sys.argv[1])
naive = NaiveBayes(formt.data())
print "Total Accuracy (cross validate) = " + str(naive.crossValidate(10))
labels = formt.channels()
counts = [[0]*len(labels) for i in range(len(labels))]
for label, data in formt.data():
expectedChn, _ = naive.predict(data)
# Confusion matrix
correct = labels.index(label)
actual = labels.index(expectedChn)
counts[correct][actual] += 1
print counts
names = ["Nearest Neighbors", "Linear SVM", "RBF SVM", "Decision Tree",
"Random Forest", "Naive Bayes", "MultinomialNB", "LDA", "QDA"]
classifiers = [
KNeighborsClassifier(3),
SVC(kernel="linear", C=0.025),
SVC(gamma=2, C=1),
DecisionTreeClassifier(max_depth=5),
RandomForestClassifier(max_depth=5, n_estimators=10, max_features=1),
#AdaBoostClassifier(),
GaussianNB(),
MultinomialNB(1.0, True),
LDA(),
QDA()]
formater = FormatJson(sys.argv[1])
data = formater.data()
x = []
y = []
for l, d in data:
x.append(d)
if l == "17 Hz":
y.append(1)
else:
y.append(0)
X = np.asarray(x)
Y = np.asarray(y)