def plot(data, inverselengthscale, labels, name_out_file, title):
# most_dominants = Utility.get_input_sensitivity(inverselengthscale, 2)
# x = data[ :, 0 ]
# y = data[ :, 1 ]
x = data[:, 0]
y = data[:, 2]
label = map(int, labels)
label = np.array(labels)
print set(labels)
# colors = ['red','green','blue','purple']
if len(labels) > 1000:
ind = np.random.choice(len(labels), 1000)
x = x[ind]
y = y[ind]
labels = labels[ind]
colors = Utility.get_color_map(len(set(labels)))
plt.clf()
# plt.scatter(x, y, c=labels, cmap=matplotlib.colors.ListedColormap(colors))
for idx, s in enumerate(sorted(set(labels))):
plt.scatter(x[labels == s], y[labels == s], c=colors[idx], label=s)
plt.legend()
plt.title(title)
plt.savefig(name_out_file)
def plot(data, inverselengthscale, labels):
most_dominants = Utility.get_input_sensitivity(inverselengthscale, 2)
x = data[ :, most_dominants[0] ]
y = data[ :, most_dominants[1] ]
label = map(int, labels)
label = np.array(labels)
print set(labels)
colors = Utility.get_color_map(len(set(labels)))
plt.clf()
for idx, s in enumerate(set(labels)):
print s
plt.scatter(x[labels==s], y[labels==s], c=colors[idx] , label=s)
plt.legend()
plt.savefig( './dbscan_test.eps' )
def plot(data, inverselengthscale, labels, name_out_file, title):
most_dominants = Utility.get_input_sensitivity(inverselengthscale, 2)
x = data[ :, most_dominants[0] ]
y = data[ :, most_dominants[1] ]
label = map(int, labels)
label = np.array(labels)
# print set(labels)
# colors = ['red','green','blue','purple']
colors = Utility.get_color_map(len(set(labels)))
plt.clf()
# plt.scatter(x, y, c=labels, cmap=matplotlib.colors.ListedColormap(colors))
for idx, s in enumerate( sorted( set(labels)) ):
plt.scatter(x[labels==s], y[labels==s], c=colors[idx] , label=s)
plt.legend()
plt.title(title)
plt.savefig( name_out_file )
def plot(x, y, outpath, label_list=None, color=None, alpha=0.5, bivariate=False, X_bi=None, Y_bi=None, title=None, xlim=None, ylim=None, cmap=None):
plt.clf()
if label_list is not None:
lab = set(label_list)
lab = list(lab)
# print lab
lab.sort(reverse=True)
label_list = np.asarray(label_list)
if color is None:
color = ['r', 'b', 'g']
color = Utility.get_color_map(len(lab))
color.reverse()
for idx, l in enumerate(lab):
# print label_list == l
plt.scatter(x[label_list == l], y[label_list == l], label=l, color=color[idx] ,alpha=0.5, cmap=cmap)
else :
plt.scatter(x, y, alpha=0.5, c=color, cmap=cmap)
if bivariate:
delta = 0.025
xx = np.arange(np.amin(X_bi), np.amax(X_bi), delta)
yy = np.arange(np.amin(Y_bi), np.amax(Y_bi), delta)
# xx = np.arange(-1.0, 1.0, delta)
# yy = np.arange(-1.0, 1.0, delta)
mux = np.mean(X_bi)
muy = np.mean(Y_bi)
sigmax = np.sqrt(np.cov(X_bi,Y_bi)[0][0])
sigmay = np.sqrt(np.cov(X_bi,Y_bi)[1][1])
sigmaxy = np.cov(X_bi,Y_bi)[0][1]
print mux, muy, sigmax, sigmay, sigmaxy
# xx = np.arange(mux-2*sigmax, mux+2*sigmax, delta)
# yy = np.arange(muy-2*sigmay, muy+2*sigmay, delta)
X, Y = np.meshgrid(xx, yy)
Z = mlab.bivariate_normal(X, Y, sigmax=sigmax, sigmay=sigmay, mux=mux, muy=muy, sigmaxy=sigmaxy)
# print Z
plt.contour(X, Y, Z)
if title is not None:
plt.title(title)
plt.legend()
if xlim:
plt.xlim(xlim)
if ylim:
plt.ylim(ylim)
print plt.xlim(), plt.ylim()
plt.savefig(outpath)
pass
