vl = load_vl(animal, fn)
cls = {tetrode: load_cl(animal, fn, tetrode) for tetrode in range(1, 17)}
''''''
# Label with task
labels = np.unique(vl['Task'])
label_is = {
contxt: np.nonzero(vl['Task'] == contxt)[0]
for contxt in labels
}
label_l = vl['Task']
t_cells = count_cells(vl, cls, trigger_tm, good_clusters)
X, Y = gpv(vl, t_cells, room_shape, bin_size, label_l, K=32)
classifier = Classifier(X, Y)
correct_dp = []
incorrect_dp = []
for i in range(len(Y)):
sbin = X[i, -1]
x = X[i, :-1]
y = Y[i, 0]
result = classifier.classify(sbin, x)
correct_dp.append(result[y])
incorrect_dp.append(result[-1 * y])
# Process
correct_dp = np.array(correct_dp)
incorrect_dp = np.array(incorrect_dp)
def generate_DP_accuracy_graph():
animal = 66
session = 60 # This is August 7, 2013 run
fn, trigger_tm = load_mux(animal, session)
vl = load_vl(animal, fn)
cls = {tetrode: load_cl(animal, fn, tetrode) for tetrode in range(1, 17)}
room_shape = [[-60, 60], [-60, 60]]
bin_size = 8
# Actual Contexts
'''
cached = try_cache(Classifier,Classifier.name,vl,cls,trigger_tm,label_is,room_shape,bin_size)
if cached is not None:
classifier, Xs, Ys = cached
logging.info('Got classifier and population vectors from Cache.cache.')
else:
classifier = Classifier(vl,cls,trigger_tm, label_is, room_shape, bin_size)
Xs, Ys = classifier.generate_population_vectors()
store_in_cache(Classifier,Classifier.name,vl,cls,trigger_tm,label_is,room_shape,bin_size,
[classifier,Xs,Ys])'''
# Label based on task
'''
labels = np.unique(vl['Task'])
label_is = {contxt: np.nonzero(vl['Task']==contxt)[0] for contxt in labels}
label_l = vl['Task']'''
''''''
label_l = get_orientation(vl, cntrx=0, cntry=0)
labels = np.unique(label_l)
label_is = {contxt: np.nonzero(label_l == contxt)[0] for contxt in labels}
classifier = Classifier(vl, cls, trigger_tm, label_is, room_shape,
bin_size)
Xs, Ys = classifier.generate_population_vectors(label_l)
correct_dp = []
incorrect_dp = []
for (xbin, ybin), vecs, lbls in zip(Xs.keys(), Xs.values(), Ys.values()):
for vec, lbl in zip(vecs, lbls):
if lbl == 0:
crct, incrct = classifier.classifiy(xbin, ybin, vec)
else:
incrct, crct = classifier.classifiy(xbin, ybin, vec)
correct_dp.append(crct)
incorrect_dp.append(incrct)
# Process
correct_dp = np.array(correct_dp)
incorrect_dp = np.array(incorrect_dp)
nonzero_is = (correct_dp > 0) | (incorrect_dp > 0)
correct_dp = correct_dp[np.nonzero(nonzero_is)[0]]
incorrect_dp = incorrect_dp[np.nonzero(nonzero_is)[0]]
from matplotlib import pyplot as plt
# 2d Histogram
plt.figure()
hist, xedges, yedges = np.histogram2d(correct_dp, incorrect_dp, 150)
Xs, Ys = np.meshgrid(xedges, yedges)
grph = plt.pcolor(Xs, Ys, hist)
plt.xlim([0, xedges[-1]])
plt.ylim([0, yedges[-1]])
plt.colorbar(grph, extend='both')
plt.title('Dot Product Classifier Accuracy')
plt.xlabel('Population vector x Correct Template')
plt.ylabel('Population vector x Incorrect Template')
# Accuracy meter
plt.figure()
accuracy = correct_dp / np.sqrt(correct_dp**2 + incorrect_dp**2)
plt.hist(accuracy, normed=True)
plt.xlabel('Accuracy')
plt.title(classifier.name)
msg = []
for i in [1, 50, 75, 90, 95, 99]:
perc = 1.0 * np.sum(accuracy > i / 100.0) / len(accuracy) * 100.0
msg.append('>%i%%: %.1f%%' % (i, perc))
msg = '\n'.join(msg)
plt.xlim([0, 1])
xcoord = plt.xlim()[0] + (plt.xlim()[1] - plt.xlim()[0]) * .1
ycoord = plt.ylim()[0] + (plt.ylim()[1] - plt.ylim()[0]) * .5
plt.text(xcoord, ycoord, msg)
plt.show()
def generate_DP_accuracy_graph():
animal = 66
session = 60 # This is August 7, 2013 run
fn, trigger_tm = load_mux(animal, session)
vl = load_vl(animal,fn)
cls = {tetrode:load_cl(animal,fn,tetrode) for tetrode in range(1,17)}
room_shape = [[-60,60],[-60,60]]
bin_size = 8
# Actual Contexts
'''
cached = try_cache(Classifier,Classifier.name,vl,cls,trigger_tm,label_is,room_shape,bin_size)
if cached is not None:
classifier, Xs, Ys = cached
logging.info('Got classifier and population vectors from Cache.cache.')
else:
classifier = Classifier(vl,cls,trigger_tm, label_is, room_shape, bin_size)
Xs, Ys = classifier.generate_population_vectors()
store_in_cache(Classifier,Classifier.name,vl,cls,trigger_tm,label_is,room_shape,bin_size,
[classifier,Xs,Ys])'''
# Label based on task
'''
labels = np.unique(vl['Task'])
label_is = {contxt: np.nonzero(vl['Task']==contxt)[0] for contxt in labels}
label_l = vl['Task']'''
''''''
label_l = get_orientation(vl,cntrx=0,cntry=0)
labels = np.unique(label_l)
label_is = {contxt: np.nonzero(label_l==contxt)[0] for contxt in labels}
classifier = Classifier(vl,cls,trigger_tm, label_is, room_shape, bin_size)
Xs, Ys = classifier.generate_population_vectors(label_l)
correct_dp = []
incorrect_dp = []
for (xbin,ybin),vecs,lbls in zip(Xs.keys(),Xs.values(),Ys.values()):
for vec,lbl in zip(vecs,lbls):
if lbl == 0:
crct, incrct = classifier.classifiy(xbin, ybin, vec)
else:
incrct, crct = classifier.classifiy(xbin, ybin, vec)
correct_dp.append(crct)
incorrect_dp.append(incrct)
# Process
correct_dp = np.array(correct_dp)
incorrect_dp = np.array(incorrect_dp)
nonzero_is = (correct_dp > 0) | (incorrect_dp > 0)
correct_dp = correct_dp[np.nonzero(nonzero_is)[0]]
incorrect_dp = incorrect_dp[np.nonzero(nonzero_is)[0]]
from matplotlib import pyplot as plt
# 2d Histogram
plt.figure()
hist,xedges,yedges = np.histogram2d(correct_dp, incorrect_dp, 150)
Xs, Ys = np.meshgrid(xedges, yedges)
grph = plt.pcolor(Xs,Ys,hist)
plt.xlim([0,xedges[-1]])
plt.ylim([0,yedges[-1]])
plt.colorbar(grph, extend='both')
plt.title('Dot Product Classifier Accuracy')
plt.xlabel('Population vector x Correct Template')
plt.ylabel('Population vector x Incorrect Template')
# Accuracy meter
plt.figure()
accuracy = correct_dp / np.sqrt(correct_dp**2+incorrect_dp**2)
plt.hist(accuracy,normed=True)
plt.xlabel('Accuracy')
plt.title(classifier.name)
msg = []
for i in [1,50,75,90,95,99]:
perc = 1.0*np.sum(accuracy > i/100.0)/len(accuracy)*100.0
msg.append('>%i%%: %.1f%%'%(i,perc))
msg = '\n'.join(msg)
plt.xlim([0,1])
xcoord = plt.xlim()[0] + (plt.xlim()[1]-plt.xlim()[0])*.1
ycoord = plt.ylim()[0] + (plt.ylim()[1]-plt.ylim()[0])*.5
plt.text(xcoord,ycoord,msg)
plt.show()
fn, trigger_tm = load_mux(animal, session)
vl = load_vl(animal,fn)
cls = {tetrode:load_cl(animal,fn,tetrode) for tetrode in range(1,17)}
''''''
# Label with task
labels = np.unique(vl['Task'])
label_is = {contxt: np.nonzero(vl['Task']==contxt)[0] for contxt in labels}
label_l = vl['Task']
t_cells = count_cells(vl,cls,trigger_tm,good_clusters)
X, Y = gpv(vl, t_cells, room_shape, bin_size, label_l, K=32)
classifier = Classifier(X,Y)
correct_dp = []
incorrect_dp = []
for i in range(len(Y)):
sbin = X[i,-1]
x = X[i,:-1]
y = Y[i,0]
result = classifier.classify(sbin,x)
correct_dp.append(result[y])
incorrect_dp.append(result[-1*y])
# Process
correct_dp = np.array(correct_dp)
incorrect_dp = np.array(incorrect_dp)
