def plot_influence(conf, mse_values, prediction_times, complexities):
"""
Plot influence of model complexity on both accuracy and latency.
"""
pl.figure(figsize=(12, 6))
host = host_subplot(111, axes_class=Axes)
pl.subplots_adjust(right=0.75)
par1 = host.twinx()
host.set_xlabel('Model Complexity (%s)' % conf['complexity_label'])
y1_label = conf['prediction_performance_label']
y2_label = "Time (s)"
host.set_ylabel(y1_label)
par1.set_ylabel(y2_label)
p1, = host.plot(complexities, mse_values, 'b-', label="prediction error")
p2, = par1.plot(complexities, prediction_times, 'r-', label="latency")
host.legend(loc='upper right')
host.axis["left"].label.set_color(p1.get_color())
par1.axis["right"].label.set_color(p2.get_color())
pl.title('Influence of Model Complexity - %s' % conf['estimator'].__name__)
pl.show()
def plot_influence(conf, mse_values, prediction_times, complexities):
# Plot influence of model complexity on both accuracy and latency.
plt.figure(figsize = (12, 6))
host = host_subplot(111, axes_class = Axes)
plt.subplots_adjust(right=0.75)
par1 = host.twinx()
host.set_xlabel('Model Complexity (%s)' % conf['complexity_label'])
y1_label = conf['prediction_performance_label']
y2_label = 'Time (s)'
host.set_ylabel(y1_label)
par1.set_ylabel(y2_label)
p1, = host.plot(complexities, mse_values, 'b-', label='prediciton error')
p2, = par1.plot(complexities, prediction_times, 'r-', label='latency')
host.legend(loc = 'upper right')
host.axis['left'].label.set_color(p1.get_color())
par1.axis['right'].label.set_color(p2.get_color())
plt.title('Influence of Model Complexity - %s' % conf['estimator'].__name__)
plt.show()
def plot_influence(conf, mse_values, prediction_times, complexities):
"""
Plot influence of model complexity on both accuracy and latency.
"""
pl.figure(figsize=(12, 6))
host = host_subplot(111, axes_class=Axes)
pl.subplots_adjust(right=0.75)
par1 = host.twinx()
host.set_xlabel("Model Complexity (%s)" % conf["complexity_label"])
y1_label = conf["prediction_performance_label"]
y2_label = "Time (s)"
host.set_ylabel(y1_label)
par1.set_ylabel(y2_label)
p1, = host.plot(complexities, mse_values, "b-", label="prediction error")
p2, = par1.plot(complexities, prediction_times, "r-", label="latency")
host.legend(loc="upper right")
host.axis["left"].label.set_color(p1.get_color())
par1.axis["right"].label.set_color(p2.get_color())
pl.title("Influence of Model Complexity - %s" % conf["estimator"].__name__)
pl.show()
# Do the work
for i in range(0,numOfChunks,step):
yield sequence[i:i+winSize]
r=csv.reader(open(sys.argv[1]))
data = [row for row in r]
msec_array = [row[1] for row in data]
diff_array = [float(row[2]) for row in data]
dev_array = abs(diff(diff_array))
#calculate mean values
mean_diff = array(diff_array, dtype=float).mean()
mean_dev = array(dev_array, dtype=float).mean()
host = host_subplot(111)
#par = host.twiny()
host.set_xlabel(u"idő (ms)")
host.set_ylabel(u"elétérés (pixel érték)")
#plot diff_array
host.plot(msec_array, diff_array, color='r')
#plot(msec_array[:len(diff_array)], [mean_diff*5 for i in range(len(diff_array))], color='r')
#plot dev_array
host.plot(msec_array[:-1], dev_array, color='b')
host.plot(msec_array[:len(dev_array)], [mean_dev*5 for i in range(len(dev_array))], color='b')
plt.grid(True)
