from sklearn.metrics import confusion_matrix
import anomalydensity as an #user defined module
#reading csv with pandas as np.loadtxt throws error if column names are present
xtrain = pd.read_csv('xtrain.csv',delimiter=',') #1000*11 training set
xtest = pd.read_csv('xtest.csv',delimiter=',') #1000*11 testing set
ytest = pd.read_csv('ytest.csv',delimiter=',') #1000*1 original y values
#converting from pandas dataframe to numpy array
xtrain1 =np.array(xtrain)
xtest1 = np.array(xtest)
ytest1 = np.array(ytest)
#creating object1
b = an.anomaly(xtrain1) #anomaly is a class inside anomalydensity module
par = b.parameter()
p_density=b.multivariateGaussian(par[0],par[2])
#creating object2
c=an.anomaly(xtest1)
pval_density=c.multivariateGaussian(par[0],par[2])
print p_density.shape,pval_density.shape
type(p_density),type(pval_density)
epsilon = np.mean(pval_density)
bestepsilon = 0.
bestf1=0.
f1=0.
pred=np.empty([1000,])
#converting from pandas dataframe to numpy array
xtrain2 =np.array(xtrain_s)
xtest2 = np.array(xtest_s)
ytest2 = np.array(ytest_s)
matplotlib.rcParams['xtick.direction'] = 'out'
matplotlib.rcParams['ytick.direction'] = 'out'
#Initializing the plot
xplt = np.arange(0, 36, 0.5)
yplt = np.arange(0,36,0.5)
X, Y = np.meshgrid(xplt, yplt)
H = np.column_stack((X.flatten(1),Y.flatten(1)))
#creating object1
b1 = an.anomaly(xtrain2) #anomaly is a class inside anomalydensity module
par1 = b1.parameter()
p_density1=b1.multivariateGaussian(par1[0],par1[2])
#creating object for plot
plt1 = an.anomaly(H) #anomaly is a class inside anomalydensity module
plt_density=plt1.multivariateGaussian(par1[0],par1[2])
plt_density.resize((72,72))
plt_density.shape
levels = [1.0000e-020,1.0000e-017,1.0000e-014,1.0000e-011,1.0000e-008,1.0000e-005,1.0000e-002]
fig=plt.figure(figsize=(30,20))
ax1 = fig.add_subplot(121)
ax1.scatter((xtrain2[:,0]),(xtrain2[:,1]),color='blue',s=5,edgecolor='none')
ax1.set_xlabel('Latency')
ax1.set_ylabel('Throughput')
import scipy as sp
from sklearn.metrics import confusion_matrix
import anomalydensity as an #user defined module
#reading csv with pandas as np.loadtxt throws error if column names are present
xtrain = pd.read_csv('xtrain.csv', delimiter=',') #1000*11 training set
xtest = pd.read_csv('xtest.csv', delimiter=',') #1000*11 testing set
ytest = pd.read_csv('ytest.csv', delimiter=',') #1000*1 original y values
#converting from pandas dataframe to numpy array
xtrain1 = np.array(xtrain)
xtest1 = np.array(xtest)
ytest1 = np.array(ytest)
#creating object1
b = an.anomaly(xtrain1) #anomaly is a class inside anomalydensity module
par = b.parameter()
p_density = b.multivariateGaussian(par[0], par[2])
#creating object2
c = an.anomaly(xtest1)
pval_density = c.multivariateGaussian(par[0], par[2])
print p_density.shape, pval_density.shape
type(p_density), type(pval_density)
epsilon = np.mean(pval_density)
bestepsilon = 0.
bestf1 = 0.
f1 = 0.
pred = np.empty([
