def renew_centroid_for_four(self, theta):
central = []
weight = []
for j in range(6):
if (theta >= 0):
so = centroid_t_to_f(self.oildrums[j].length,
self.oildrums[j].width,
self.oildrums[j].height,
self.oildrums[j].volume, theta)
p = so.solve()
weight.append(self.oildrums[j].weight)
t = ut.threeDim2threeDim(p, self.oildrums[j].get_xyz(),
self.oildrums[j].get_lwh())
central.append(ut.point(t[0], t[1], t[2]))
else:
weight.append(self.oildrums[j].weight)
so = centroid_t_to_f(self.oildrums[j].length,
self.oildrums[j].width,
self.oildrums[j].height,
self.oildrums[j].volume, math.fabs(theta))
p = so.solve()
tmp = point(self.oildrums[j].length - p.getX(), p.getY(),
p.getZ())
t = ut.threeDim2threeDim(tmp, self.oildrums[j].get_xyz(),
self.oildrums[j].get_lwh())
central.append(point(t[0], t[1], t[2]))
central.append(ut.point(0, 0, 0)) # 加上飞行器的质心
weight.append(3000) # 加上飞行器的质量
cur_weight_point = ut.centroid_of_centorids(
central, weight) #求多个质心坐标的到一个质心坐标 v point 质心坐标list t 质量
return cur_weight_point, central, weight
def truncate(
self, y
): # trunca el trapecio con la recta que se le pasa como parametro que es paralela a x
if y >= self.points[1].y:
# significa que la recta esta por encima del trapecio
return trapezoidal(self.points)
mr1 = slope(
self.points[0],
self.points[1]) # pendiente de los dos primero puntos del trapecio
mr2 = slope(
self.points[2],
self.points[3]) # pendiente de los dos ultimos puntos del trapecio
nr1 = trace(
mr1, self.points[0]
) # traza de la recta formada por los 2 primeros del trapecio
nr2 = trace(
mr2, self.points[2]
) # traza de la recta formada por los 2 ultimos del trapecio
x1 = (y -
nr1) / mr1 # x del punto de interseccion con la primera recta
x2 = (y -
nr2) / mr2 # x del punto de interseccion con la segunda recta
return trapezoidal(
[self.points[0],
point(x1, y),
point(x2, y), self.points[3]])
def truncate(
self, y
): # trunca el triangulo con una recta parametro que es paralela a x
if y >= self.points[
1].y: # significa que la recta esta por encima del triangulo y no lo trunca
return triangle(self.points)
mr1 = slope(self.points[0], self.points[1]
) # pendiente de los dos primero puntos del triangulo
mr2 = slope(self.points[1], self.points[2]
) # pendiente de los dos segundos puntos del triangulo
nr1 = trace(
mr1, self.points[0]
) # traza de la recta fromada por los 2 primeros puntos del triangulo
nr2 = trace(
mr2, self.points[1]
) # traza de la recta fromada por los 2 segundos puntos del triangulo
x1 = (y -
nr1) / mr1 # x del punto de interseccion con la primera recta
x2 = (y -
nr2) / mr1 # x del punto de interseccion con la segunda recta
return trapezoidal(
[self.points[0],
point(x1, y),
point(x2, y), self.points[2]])
def compute_vals(in_mat, out_mat): import numpy as np from utils import point a, b = np.loadtxt(in_mat), np.loadtxt(out_mat) rmse_val = str(np.sqrt(((np.matmul(a,b)-np.identity(4))**2).mean())) return point(a), point(b), rmse_val
def cal_one_kg_pw(self):
# 计算减少单位质量造成的飞行器质心偏移
v = []
v.append(point(self.ari_x, self.air_y, self.air_z))
v.append(point(0, 0, 0))
w = []
w.append(100) # 表示这个点增加100的向量的反方向
w.append(3000)
p = centroid_of_centorids(v, w)
p = point(-p.getX(), -p.getY(), -p.getZ())
# p.norm()
self.power_weight = p
def truncate(self, y): # si la recta esta por encima de la funcion
m = slope(self.points[0], self.points[1])
if (m > 0 and y >= self.points[1].y) or (m < 0
and y >= self.points[0].y):
return regular(self.points)
m = slope(self.points[0], self.points[1])
n = trace(m, self.points[0])
x = (y - n) / m
if m > 0:
return regular([self.points[0], point(x, y)])
return regular([point(x, y), self.points[1]])
def to(obj_self, end1, end2, end3, steps=100):
ctp = lambda pt: point(*pt) if type(
pt) == tuple else pt # convert tuple to point
s1, s2, s3, e1, e2, e3 = ctp(start1), ctp(start2), ctp(
start3), ctp(end1), ctp(end2), ctp(end3)
return self.jsonrpc.gesture(self.selector, s1, s2, s3, e1, e2, e3,
steps)
def main():
logreg = learner(dimensions=1, data=data, w_initial=[0, 0], rate=.07,\
varrate=False, epsilon=1e-15, maxIter=1e4)
logreg.solveLog()
print("Predict Pr(y^=0) for x=3, x=5:")
logreg.predict(utils.point(3, 0), y_hat=0)
logreg.predict(utils.point(5, 0), y_hat=0)
print("\nClassify the following points:")
logreg.classify(utils.point(1.5, 0))
logreg.classify(utils.point(2.5, 0))
logreg.classify(utils.point(3.5, 0))
logreg.classify(utils.point(4.5, 0))
logreg.classify(utils.point(5.5, 0))
logreg.classify(utils.point(6.5, 0))
logreg.classify(utils.point(7.5, 0))
result = []
for data_pre in test_data:
test_batch,condicate_id,keya = data_pre
test_data_final = prepare_data(test_batch)
#
prob2 = mol.predict_with_dict(sess, test_data_final)
#prob2,loss2, accuracy2= mol.calculate_with_dict(sess, test_data_final)
rank_ = [[i, j] for i, j in zip(prob2[0], condicate_id)]
#是x[0][0] x[0][1] 第二维度排序 tgt [0,1] 为正样本
rank = sorted(rank_, key=lambda x: x[0][1], reverse=True)
topk = utils.point(rank, keya)
result.append(topk)
cnt += 1
re = pd.DataFrame(result)
print("test:rst len ", len(re))
re.to_csv(os.path.join(RST_PATH,"result-%s.csv" % (testday)), index=False)
except Exception as e:
print(e,)
logging.debug("test cnt {}\n,error {}".format(cnt,e))
logging.debug("execept e :{}".format(traceback.format_exc()))
from Rule import rule
from utils import point
from Fuzzy_system import fuzzy_system
# Las funciones de pertenencia estan graficadas en el informe
# Ejemplo 1
# R1 aire = frio => velocidad = parada
# R2 aire = fresco => velocidad = lenta
# R3 aire = correcto => velocidad = media
# R4 aire = calido => velocidad = rapida
# R5 aire = caliente => velocidad = maxima
# Funciones de Pertenencia
aire = caracteristic("aire")
aire_frio = aire.add_regular("frio", [point(12, 1), point(15, 0)])
aire_fresco = aire.add_trapezoidal(
"fresco",
[point(12, 0), point(15, 1),
point(18, 1), point(22, 0)])
aire_correcto = aire.add_trapezoidal(
"correcto",
[point(18, 0), point(22, 1),
point(25, 1), point(27, 0)])
aire_calido = aire.add_trapezoidal(
"calido",
[point(25, 0), point(27, 1),
point(30, 1), point(32, 0)])
aire_caliente = aire.add_regular("caliente", [point(30, 0), point(32, 1)])
velocidad = caracteristic("velocidad")
# Keep asking until you get a non-empty file
if (len(classified) != 0):
getting = False;
getting = True;
while (getting):
data = input("input x y to be classified: ");
data = data.split();
# check if input is invalid
if (len(data) < 2):
print("invalid input. Please input an x, y pair in the format: 'x y'");
else:
# parse point into a point object with empty category
me = point(float(data[0]), float(data[1]));
# stop taking input if you get '1 1'
if (me.x == 1 and me.y == 1):
getting = False;
break;
# now do some calculus shit
# Calculate distance to each classified point
for item in classified:
item.distance = utils.distanceBetween(me, item);
# Sort classified by distance
classified.sort(key=lambda x: x.distance);
def solve(self):
if (self.theta == 0):
height_left = self.area / self.length
v = []
v.append(utils.point(0, 0, 0))
v.append(utils.point(self.length, 0, 0))
v.append(utils.point(self.length, 0, height_left))
v.append(utils.point(0, 0, height_left))
return utils.centroid_of_convex_polygon(v)
if (self.theta < self.a0):
# 此时分3中情况讨论
if (self.area <= self.extreme_small_volume_small_angle_area):
# 此时三角形的两个点落在直角的两条边上 x * x * tan(theta) = 2 * s
bottom = math.sqrt(2 * self.area /
math.tan(math.radians(self.theta)))
left = bottom * math.tan(math.radians(self.theta))
v = []
v.append(utils.point(0, 0, 0))
v.append(utils.point(0, 0, left))
v.append(utils.point(bottom, 0, 0))
return utils.centroid_of_convex_polygon(v)
elif self.area > self.extreme_small_volume_small_angle_area and self.area <= self.extreme_big_volume_small_angle_area:
# 此时两个点落在两条高线上 一共四个点
area_s1 = self.area - self.extreme_small_volume_small_angle_area
delta_left = area_s1 / self.length
v = []
v.append(
utils.point(
0, 0,
self.extreme_small_volume_small_angle_left_height +
delta_left))
v.append(utils.point(self.length, 0, delta_left))
v.append(utils.point(self.length, 0, 0))
v.append(utils.point(0, 0, 0))
return utils.centroid_of_convex_polygon(v)
elif self.area > self.extreme_big_volume_small_angle_area:
rect_area = self.length * self.height
delta_s = rect_area - self.area
# x * x * tan(theta) = 2 * delta_s
upper = math.sqrt(2 * delta_s /
math.tan(math.radians(self.theta)))
right = upper * math.tan(math.radians(self.theta))
v = []
v.append(utils.point(self.length - upper, 0, self.height))
v.append(utils.point(0, 0, self.height))
v.append(utils.point(0, 0, 0))
v.append(utils.point(self.length, 0, 0))
v.append(utils.point(self.length, 0, self.height - right))
return utils.centroid_of_convex_polygon(v)
else:
print('ERROR')
else:
# 角度 >= a0
# 此时分3种情况讨论
if self.area < self.extreme_small_volume_big_angle_area:
# 此时三角形的两个点落在直角的两条边上
bottom = math.sqrt(2 * self.area /
math.tan(math.radians(self.theta)))
left = bottom * math.tan(math.radians(self.theta))
v = []
v.append(utils.point(0, 0, 0))
v.append(utils.point(0, 0, left))
v.append(utils.point(bottom, 0, 0))
return utils.centroid_of_convex_polygon(v)
elif self.area >= self.extreme_small_volume_big_angle_area and self.area < self.extreme_big_volume_big_angle_area:
# 此时两个点落在两条高线上 一共四个点
area_s1 = self.area - self.extreme_small_volume_small_angle_area
delta_bottom = area_s1 / self.height
v = []
v.append(
utils.point(
self.extreme_small_volume_big_angle_bottom_point +
delta_bottom, 0, 0))
v.append(utils.point(delta_bottom, 0, self.height))
v.append(utils.point(0, 0, self.height))
v.append(utils.point(0, 0, 0))
return utils.centroid_of_convex_polygon(v)
elif self.area > self.extreme_big_volume_big_angle_area:
rect_area = self.length * self.height
delta_s = rect_area - self.area
# x * x * tan(theta) = 2 * delta_s
upper = math.sqrt(2 * delta_s /
math.tan(math.radians(self.theta)))
right = upper * math.tan(math.radians(self.theta))
v = []
v.append(utils.point(self.length - upper, 0, self.height))
v.append(utils.point(0, 0, self.height))
v.append(utils.point(0, 0, 0))
v.append(utils.point(self.length, 0, 0))
v.append(utils.point(self.length, 0, self.height - right))
return utils.centroid_of_convex_polygon(v)
else:
print('ERROR', self.area)
def main():
data = \
[utils.point(0,0,0,True),
utils.point(0,0,1,True),
utils.point(0,1,0,True),
utils.point(0,1,1,False),
utils.point(1,0,0,True),
utils.point(1,0,0,True),
utils.point(1,1,0,False),
utils.point(1,0,1,True),
utils.point(1,1,0,False),
utils.point(1,1,0,False)]
validation = \
[utils.point(0,0,0,True),
utils.point(0,1,1,True),
utils.point(1,1,0,True),
utils.point(1,0,1,False),
utils.point(1,0,0,True)]
forest = utils.forest(data)
forest.genMTreesNPoints(10, 10)
for v in validation:
forest.validate(v)
'''
Created on December 1, 2019
@author: Zoya Samsonov
'''
import utils
from learn_method import learner
# x was pre-selected iid
train_data = utils.pointdata([
utils.point([-1.18971], 11.41540),
utils.point([2.70383], 17.31071),
utils.point([3.21282], 20.32224),
utils.point([0.21780], 10.04744),
utils.point([7.38929], 64.60164),
utils.point([-0.10828], 10.01173),
utils.point([-0.65363], 10.42724),
utils.point([0.91987], 10.84616),
utils.point([4.15727], 27.28287),
utils.point([6.81239], 56.40865),
utils.point([9.24428], 95.45665),
utils.point([-1.31987], 11.74204)
])
# x was pre-selected iid
test_data = utils.pointdata([
utils.point([7.11829], 60.67008),
utils.point([2.91025], 18.46957),
utils.point([1.35384], 11.83289),
utils.point([3.24483], 20.52890),
'''
Created on October 5, 2019
@author: Zoya Samsonov
'''
from learn_method import learner
import utils
data = utils.pointdata([
utils.point(1, -1),
utils.point(2, 1),
utils.point(3, -1),
utils.point(4, 1),
utils.point(5, -1),
utils.point(6, 1),
utils.point(7, 1),
utils.point(8, 1)
])
def main():
logreg = learner(dimensions=1, data=data, w_initial=[0, 0], rate=.07,\
varrate=False, epsilon=1e-15, maxIter=1e4)
logreg.solveLog()
print("Predict Pr(y^=0) for x=3, x=5:")
logreg.predict(utils.point(3, 0), y_hat=0)
logreg.predict(utils.point(5, 0), y_hat=0)
print("\nClassify the following points:")
logreg.classify(utils.point(1.5, 0))
logreg.classify(utils.point(2.5, 0))
