def noiseImpactSearch(d,nSensor,nAnchor,sigmas,radiorange):
graph.setLimit()
for i in range(len(sigmas)):
graph.setGraph()
[posX,locX,disX,conX,noisyDisX,trueDisX,estPosX,cLevel] = problems.createProblem_random(d,nSensor,nAnchor,sigma,radiorange,0)
multi.localizeByMultilateration(nSensor,nAnchor,locX,conX,estPosX,noisyDisX,cLevel,0,0)
rmsd = helper.calcRMSD(nSensor,locX,posX,estPosX)
graph.plotPoint2D(estPosX,"")
print rmsd
graph.show()
def generate():
#lines(path, 'a', xlabel='A setting', ylabel='hits', title="First cool graph")
#bar(path, title='Second cool graph', ylabel='hits')
options = {}
dataset_lines(path, 'a', xlabel='a setting', ylabel='hits', title='a_evolution', **options)
dataset_lines(path, 'b', xlabel='b setting', ylabel='hits', title='b_evolution', **options)
dataset_lines(path, 'c', xlabel='c setting', ylabel='hits', title='c_evolution', **options)
#adjust = {'top': 0.95, 'bottom': 0.15, 'right': 0.96, 'left': 0.1}
#graph.save_figures(adjust=adjust, xscale=.7, yscale=.5)
#graph.show(adjust=adjust, xscale=.7, yscale=.5)
graph.show()
import graph
from borders import country_border_graph
from_verts = []
to_verts = []
for country in country_border_graph:
for i in range(0, len(country_border_graph[country])):
if country_border_graph[country][0] == '':
# skip islands
continue
from_verts.append(country)
to_verts.append(country_border_graph[country][i])
graph.show(from_verts, to_verts)
# Scatter A B def scatter_ab(): dot_a = ga.scatter(fpr_a[1],tpr_a[1],'A', 'o', colors[0]) dot_b = ga.scatter(fpr_b[1],tpr_b[1],'B', 'o', colors[1]) # Convex Hullfor A B C points = np.array([fpr_c,tpr_c]).T points = np.vstack((points,np.array([fpr_a[1],tpr_a[1]]))) points = np.vstack((points,np.array([fpr_b[1],tpr_b[1]]))) points = np.vstack((points,np.array([0,0]))) points = np.vstack((points,np.array([1,1]))) hull = ConvexHull(points) for simplex in hull.simplices: plt.plot(points[simplex, 0], points[simplex, 1], 'k-') ga.show() # plot connecting line of C # Scatter series of C for f,t, thres,color in zip(fpr_c,tpr_c,thresholds_c,colors): l = 'C: ' + str(thres) # plt.scatter(f,t,marker = '>', color = color, label = 'C: ' + str(thres)) ga.scatter(f,t,l,'>',color) scatter_ab() plt.plot(fpr_c,tpr_c) ga.show() # For c_threshold == 0.4 p = ca.check_threshold(c_value,0.4)
roc_curve = []
#for threshold, label in sorted(map(ThresholdAndLabel, samples), key = lambda x: x[0], reverse=True):
for threshold, label in sorted(map(ThresholdAndLabel, samples), key = lambda x: x, reverse=True):
roc_curve.append( label in known_labels )
graph.roc(roc_curve, label = title)
pass
plot_roc(threshold = density_threshold,
samples = test_data,
title= 'ROC for P(x|c) threshold')
plot_roc(threshold = semi_threshold,
samples = test_data,
title= 'ROC for P(x|c)/P(x) threshold')
graph.savefig('example1.pdf')
graph.show()
"""Input space is small, so we can analyze it."""
def analyse(threshold, samples):
for a in sorted(zip(map(threshold, samples), samples), reverse=True):
print(a)
inputs = list(set(unlabelled_data))
print('P(x|c) Threshold')
analyse(density_threshold, inputs)
print('P(x|c)/P(x) Threshold')
analyse(semi_threshold, inputs)
import quandl_dump as qd
import fracture
import graph
#('GOOGL', 'MSFT', 'TSLA', 'CSCO', 'TWTR', 'FB', 'GOOD', 'GLAD', 'FIX', 'NUE', 'XOMA','HELE')
companies = qd.get_stored()
for c in companies:
result = fracture.get_point(c[1][['Adj. Close']])
print(c[0], result)
if result:
graph.show(c[0], c[1], result)
# companies = qd.get_list("NASDAQ")
# qd.dump(companies)
newGraph)
# end timer
end = time.time()
# calculate total runtime
runtime += (end - start) * 1000
# calculate average runtime
runtime = runtime / numTrials
# add them to the appropriate arrays
x_values.append(n)
y_values.append(runtime)
return x_values, y_values
xvalues = [10, 50, 100, 150, 200, 300, 400, 500, 700, 1000, 1200, 1400, 1600]
x_axis, y_axis = runDijkstraTrials(dijkstra_helper, xvalues)
x_axis2, y_axis2 = runAStarTrials(a_star_helper, xvalues)
# plotting the graph
graph.plot(x_axis, y_axis, "-.", color="blue", label="Dijkstra algorithm")
graph.plot(x_axis2, y_axis2, "-", color="red", label="A Star Algorithm")
# title and legend
graph.title("Running times of Dijkstra's vs A* Algorithm")
graph.legend()
# x and y labels for the graph
graph.xlabel("n")
graph.ylabel("Time(ms)")
graph.show()
# I love to play around with colors :)
accent_color = '#c9c9c9'
indicators_color = '#598720'
# The use of normalized data is necessary for plotting the price and moving averages in the same graph.
data['Close'] = data_n['Close']
data[ci.moving_average_1_label] = data_n[ci.moving_average_1_label]
data[ci.moving_average_2_label] = data_n[ci.moving_average_2_label]
# data['ATR'] = data_n['ATR']
# data['MACD'] = data_n['MACD']
# data['Stochastics'] = data_n['Stochastics']
# data['RSI'] = data_n['RSI']
# Draw
draw(ticker,
data[dataset_train_length:],
predicted_data,
ci,
draw_moving_average_1=draw_moving_average_1,
draw_moving_average_2=draw_moving_average_2,
draw_ATR=draw_ATR,
draw_MACD=draw_MACD,
draw_Stochastics=draw_Stochastics,
draw_RSI=draw_RSI,
accent_color=accent_color,
indicators_color=indicators_color)
show()
# save('graph.png')