def graph(self):
if type(self.funct) == type(Function('')):
graph(self.funct)
elif type(self.funct) == type(Equation('')):
graph(self.funct[1])
else:
graph_par(self.funct)
def do_POST(self):
content_length = int(self.headers['Content-Length'])
body = self.rfile.read(content_length)
self.send_response(200)
self.end_headers()
with open(file_path, "wb") as file:
file.write(body)
grapher.graph(file_path, output_path)
with open(output_path, "rb") as file:
self.wfile.write(file.read())
print(time.now().time(), "-> Success at ip:", self.client_address[0])
def graph(xText, yText, regression, regBox):
#print(len(xText) ,len(yText) )
if (len(regression) == 0):
tk.messagebox.showerror("Error", "Pick a regression type")
return
if (len(xText) != len(yText)) or len(xText) == 0:
tk.messagebox.showerror("Error,", "Missing points")
return
print("Graphing " + str(len(xText))+ " data points")
graph = grapher.graph(len(xText))
graph.numberOfPoints = len(xText)
for i in range(len(xText)):
try:
graph.points[i,0] = float(xText[i])
except ValueError:
tk.messagebox.showerror("Error", "All points must be numbers")
return
try:
graph.points[i,1] = float(yText[i])
except ValueError:
tk.messagebox.showerror("Error", "All points must be numbers")
return
graph.regression = regression[0]+1
#print(graph.regression)
graph.coefficients = graph.polyFit(graph.regression)
# print(graph.calculatedFunction(2))
print(graph.points[:,1])
print(graph.rSquaredCalculate())
regBox.configure(text = graph.polyLabel())
graph.execute()
return
def update_graph(name):
person = name
words_me, words_you = words_used(data[person.replace(" ", "_")], name_fix)
times_info, msgs_info_first_me, msgs_info_last_me, msgs_info_last_you, msgs_info_first_you = ms.get_msgs_time(
save[person.replace(" ", "_")])
fig4 = graph(words_me, words_you, person)
fig6 = ms.plot_overtime(times_info, 2)
return fig4, fig6, msgs_info_last_me, msgs_info_last_you, msgs_info_first_me, msgs_info_first_you
def graph(request, city1, city2, field):
template = loader.get_template('graph/graph.html')
graph_html = grapher.graph(city1, city2, field)
if (graph_html == None):
return redirect('/graph/')
graph_html = urllib.urlopen(graph_html).read()
context = {
"graph_html": graph_html,
"city1": city1,
"city2": city2,
"field": field,
}
return HttpResponse(template.render(context, request))
def prepare_graph(regression_type):
xPoints = Data.query.with_entities(Data.xValues)
yPoints = Data.query.with_entities(Data.yValues)
xPoints = [x for x, in xPoints]
yPoints = [y for y, in yPoints]
if len(xPoints) <= regression_type:
print('error points regression_type')
raise ValueError('Not enough points for regression')
graph = gr.graph(len(xPoints))
graph.numberOfPoints = len(xPoints)
for i in range(len(xPoints)):
graph.points[i, 0] = xPoints[i]
graph.points[i, 1] = yPoints[i]
graph.regression = regression_type
graph.coefficients = graph.polyFit(graph.regression)
function = graph.getCoeff()
rSquared = graph.rSquaredCalculate()
#print(function)
#print(rSquared)
#return graph.graphToHtml()
return graph.graphToHtml(), function, rSquared
import math
import sys
import grapher
points_input = sorted(
points_input,
key=lambda x: x[1]) # sort points by y coordinates from least to greatest
dimensions = (
sorted(points_input, key=lambda x: x[0])[-1][0], points_input[-1][1]
) # (max_x, max_y) = (sort by x then grab last (largest) x value, take y-sorted list and grab last (largest) value)
point_vectors = []
precision = 2
grapher.graph(points_input)
print("")
def dist(point1, point2):
# euclidean distance formula d = √((ay-by)^2+(ax-bx)^2)
return round(
math.sqrt((point1[0] - point2[0])**2 + (point1[1] - point2[1])**2),
precision)
def heading(point1, point2, dist):
if not dist: return 0
elif point2[0] == point1[0]: return int(not (point2[1] > point1[1])) * 180
elif point2[1] == point1[1]:
return int(not (point2[0] > point1[0])) * 180 + 90
import classifier
import neuralpy
import grapher
net = neuralpy.Network(2, 8, 1)
uris = [ "miller_xml/" + str(i) + ".xml" for i in range(1,13) ]
epochs = 200
learning_rate = 0.05
validation_percentage = .32
ps = []
classifier.stand = "L"
for i in range(0, 10):
net.randomize_parameters()
p = classifier.train(net, uris, epochs, learning_rate, validation_percentage, save_file='results/miller_' + str(i) + '.txt')
neuralpy.output(p)
ps.append(p)
i = ps.index(max(ps))
neuralpy.output("\n\n" + str(max(ps)) + " at " + str(i))
grapher.graph(filepath='results/miller_' + str(i) + '.txt')
# grapher.graph(filepath='results/miller_4.txt')
points_input = [[3,10],[8,1],[3,5],[1,4],[8,10],[4,5]]
### Begin code
import math
import sys
import grapher
points_input = sorted(points_input, key=lambda x: x[1]) # sort points by y coordinates from least to greatest
dimensions = (sorted(points_input, key=lambda x: x[0])[-1][0] , points_input[-1][1]) # (max_x, max_y) = (sort by x then grab last (largest) x value, take y-sorted list and grab last (largest) value)
point_vectors = []
precision = 2
grapher.graph(points_input)
print("")
def dist(point1, point2):
# euclidean distance formula d = √((ay-by)^2+(ax-bx)^2)
return round(math.sqrt((point1[0]-point2[0])**2+(point1[1]-point2[1])**2), precision)
def heading(point1, point2, dist):
if not dist: return 0
elif point2[0] == point1[0]: return int(not (point2[1] > point1[1]))*180
elif point2[1] == point1[1]: return int(not (point2[0] > point1[0]))*180+90
else:
return round(math.degrees(math.cos((point2[0]-point1[0])/dist)))
import grapher grapher.graph(None, f="test.txt")
# counts number of true and false values
Accurate_Predicted_Total = compared["Match"].value_counts()
# calculates actual/experimental %
Accurate_Predicted_Total["Actual Percentage"] = (
Accurate_Predicted_Total["True"] / compared["Index"].max()) * 100
# grabs theoretical % from score variable above
Accurate_Predicted_Total["Theoretical Percentage"] = score * 100
# calculates percent error (actual-theoretical)/theoretical
# NOTE: any value of absolute value should bre read as positive even if negative
Accurate_Predicted_Total["Percent Error"] = (
((score * 100) -
(Accurate_Predicted_Total["True"] / compared["Index"].max()) * 100) /
(score * 100)) * 100
Accurate_Predicted_Total["Percent Error"] = math.fabs(
Accurate_Predicted_Total["Percent Error"])
Accurate_Predicted_Total.to_csv(path_or_buf="data/ml/stats/Keystats.csv")
from grapher import graph
graph()
import pickle
pkl_filename = "pickle_model.pkl"
with open(pkl_filename, 'wb') as file:
pickle.dump(clf, file)
# loading data
# with open(pkl_filename, 'rb') as file:
# pickle_model = pickle.load(file)