Upcoming Tennis W35 Aldershot Matches: What to Expect
Tomorrow's W35 Aldershot tournament promises an exhilarating day of tennis, showcasing some of the most talented players in the category. With a diverse field of competitors, fans and bettors alike have much to look forward to. This guide provides an in-depth look at the scheduled matches, player profiles, and expert betting predictions to help you navigate the excitement.
Match Schedule Highlights
The W35 Aldershot tournament features several key matchups that are expected to draw significant attention. Below is a snapshot of the most anticipated matches:
- Match 1: Player A vs. Player B - A classic rivalry rekindled, this match is set to be a thrilling contest between two seasoned competitors.
- Match 2: Player C vs. Player D - Known for their aggressive playstyles, both players promise a fast-paced and dynamic encounter.
- Match 3: Player E vs. Player F - With contrasting styles, this match could go either way, making it a must-watch for enthusiasts.
Player Profiles
Player A: The Veteran's Edge
With years of experience under her belt, Player A is known for her strategic play and mental toughness. Her ability to adapt to different opponents makes her a formidable opponent on any court.
- Strengths: Consistent baseline play, strong defensive skills.
- Weaknesses: Susceptible to aggressive net play.
Player B: The Rising Star
Player B has been making waves in the tennis circuit with her powerful serves and quick reflexes. Her recent performances have positioned her as one of the players to watch in this tournament.
- Strengths: Powerful serve, excellent footwork.
- Weaknesses: Inconsistent backhand under pressure.
Player C: The Aggressive Contender
Known for her aggressive baseline play, Player C thrives in high-pressure situations. Her ability to dictate points from the baseline makes her a challenging opponent.
- Strengths: Aggressive baseline play, strong forehand.
- Weaknesses: Vulnerable on serve.
Betting Predictions and Insights
For those interested in placing bets, here are some expert predictions and insights based on current form and head-to-head records:
Match 1: Player A vs. Player B
Given Player A's experience and tactical prowess, she is slightly favored to win. However, Player B's recent form cannot be ignored.
- Prediction: Player A to win in straight sets.
- Betting Tip: Consider a small bet on Player B for an upset.
Match 2: Player C vs. Player D
This match is expected to be highly competitive. Both players have similar playing styles, which could lead to an intense rally battle.
- Prediction: Match going to three sets.
- Betting Tip: Look for opportunities on set bets or total games over/under.
Tournament Trends and Statistics
Analyzing past performances and current trends can provide valuable insights into potential outcomes. Here are some key statistics to consider:
- Average Match Length: Matches are expected to average around two hours, with longer rallies contributing to extended playtimes.
- Serve Win Percentage: Players with higher serve win percentages tend to perform better in this tournament setting.
- Aces Per Match: Players with a high number of aces per match often have an edge in crucial tiebreak situations.
Tips for Watching Live
mattgao/gym-lander<|file_sep|>/gym_lander/envs/lander_env.py
import numpy as np
import gym
from gym import spaces
from gym.utils import seeding
from gym.envs.classic_control import rendering
class LanderEnv(gym.Env):
metadata = {
'render.modes': ['human', 'rgb_array'],
'video.frames_per_second': int(np.round(1 / .01))
}
def __init__(self):
self.gravity = -9.81
self.drag = .01
self.dt = .01
self.x_threshold = (6 * self.dt) ** .5
self.y_threshold = (12 * self.dt) ** .5
self.action_space = spaces.Box(low=np.array([0., -1]),
high=np.array([1., +1]),
dtype=np.float32)
self.observation_space = spaces.Box(low=-np.inf,
high=np.inf,
shape=(8,),
dtype=np.float32)
# angle limits
self.min_pitch_angle = -.6
self.max_pitch_angle = +.6
# thrust limits
self.min_thrust = .5
self.max_thrust = +1.
# fuel limits
self.min_fuel = .05
self.max_fuel = +1.
# max number of steps per episode
self.max_steps = int(2**8)
# seed random number generator
self.np_random, _seed = seeding.np_random()
# start new episode
self.reset()
def seed(self, seed=None):
return [self.np_random.seed(seed)]
def reset(self):
# reset state variables
self.steps_beyond_done = None
# randomize starting location and angle within specified ranges
init_pos_range_x = (self.x_threshold * .6,
self.x_threshold * .4)
init_pos_range_y = (self.y_threshold * .6,
self.y_threshold * .4)
init_angle_range_x = (self.min_pitch_angle * .6,
self.min_pitch_angle * .4)
init_angle_range_y = (self.max_pitch_angle * .6,
self.max_pitch_angle * .4)
x_pos = self.np_random.uniform(init_pos_range_x[0],
init_pos_range_x[1])
y_pos = self.np_random.uniform(init_pos_range_y[0],
init_pos_range_y[1])
x_vel = np.cos(self.np_random.uniform(0., np.pi)) *
np.sqrt(-2. * self.gravity * y_pos)
y_vel = np.sin(self.np_random.uniform(0., np.pi)) *
np.sqrt(-2. * self.gravity * y_pos)
angle_cosine = np.cos(self.np_random.uniform(init_angle_range_x[0],
init_angle_range_x[1]))
angle_sine = np.sin(self.np_random.uniform(init_angle_range_y[0],
init_angle_range_y[1]))
angle_vel = (self.np_random.rand() - .5) / (.3 * np.pi)
fuel_level = self.max_fuel
# reset state variables using randomized values above
state_vars_dict = {'x': x_pos,
'y': y_pos,
'vx': x_vel,
'vy': y_vel,
'cosA': angle_cosine,
'sinA': angle_sine,
'omega': angle_vel,
'fuel_level': fuel_level}
for k,v in state_vars_dict.items():
setattr(self,k,v)
# reset render view position using randomized x position value above
if hasattr(self.viewer,'world'):
x_view_pos_offset_min,max_offset=-(self.x_threshold*.75),
(self.x_threshold*.75)
y_view_pos_offset_min,max_offset=-(self.y_threshold*.75),
(self.y_threshold*.75)
x_view_pos_offset=self.np_random.uniform(x_view_pos_offset_min,
max_offset)
y_view_pos_offset=self.np_random.uniform(y_view_pos_offset_min,
max_offset)
offset=(x_view_pos_offset,y_view_pos_offset)
self.viewer.world.shift_camera(offset)
return np.array([self.x,self.y,self.vx,self.vy,self.cosA,self.sinA,
self.omega,self.fuel_level])
def step(self,action):
assert action.shape == (2,)
assert action[0] <= action[1], "invalid action"
done=False
# apply control input and update state variables
thrust=action[0]*self.max_thrust+(action[1]-action[0])*(
(self.max_thrust-self.min_thrust)/2.)
fuel_burn_rate=.01*thrust
delta_omega=.04*thrust*(action[1]-action[0])
thrust_vec=(thrust*self.cosA,-thrust*self.sinA)
if fuel_burn_rate > getattr(self,'fuel_level',None):
thrust=thrust*(getattr(self,'fuel_level',None)/fuel_burn_rate)
fuel_burn_rate=getattr(self,'fuel_level',None)
vx=(getattr(self,'vx',None)+
thrust_vec[0]*self.dt-self.drag*getattr(self,'vx',None)*
abs(getattr(self,'vx',None))*self.dt)/
(1.+self.drag*abs(getattr(self,'vx',None))*self.dt)
vy=(getattr(self,'vy',None)+
thrust_vec[1]*self.dt+(getattr(self,'y',None)*self.gravity)*
self.dt-self.drag*getattr(self,'vy',None)*
abs(getattr(self,'vy',None))*self.dt)/
(1.+self.drag*abs(getattr(self,'vy',None))*self.dt)
omega=(getattr(self,'omega',None)+delta_omega*self.dt)/
(1.+abs(delta_omega)*self.dt)
cosA=getattr(self,'cosA',None)+np.sin(omega)*self.dt/2.
sinA=getattr(self,'sinA',None)-np.cos(omega)*self.dt/2.
cos_sq,sin_sq=cosA**2,sinA**2
norm=sin_sq+cos_sq
if norm==0:
cosA,sinA=0.,1.
elif norm!=1:
cosA/=np.sqrt(norm)
sinA/=np.sqrt(norm)
x=getattr(self,'x',None)+((getattr(self,'vx',None)+
thrust_vec[0]*self.dt/self.mass)*
getattr(self,'dt',None))
y=getattr(self,'y',None)+((getattr(self,'vy',None)+
thrust_vec[1]*self.dt/self.mass+
getattr(self,'y',None)*
getattr(self,'gravity',None)*
getattr(self,'dt',None))/2.)
fuel_level=getattr(self,'fuel_level',None)-fuel_burn_rate*self.dt
if not hasattr(self,'mass'):
mass=self.mass_init-fuel_level*(self.mass_init-self.mass_final)
if x >= -self.x_threshold and x <= +self.x_threshold
and y >= -self.y_threshold
and y <= +(.5*self.y_threshold):
done=True
reward=100.+10.*fuel_level
if abs(x) <= (.05*self.x_threshold)
and abs(y-.5*self.y_threshold) <= (.05*.5*self.y_threshold)
and abs(vx) <= (.5*self.x_threshold/self.t_slow)
and abs(vy) <= (.5*.5*self.y_threshold/self.t_slow):
reward+=100.
if abs(omega) <= (.2*np.pi/self.t_slow)
and abs(vy+.5*self.gravity*self.t_slow) <= (.5*.5*
self.y_threshold/self.t_slow**2):
reward+=100.
if abs(thrust-self.hover_thrust)<=(.01*self.hover_thrust):
reward+=100.
if abs(cos_sq-.5)<=(.05/2.):
reward+=100.
if abs(sin_sq-.5)<=(.05/2.):
reward+=100.
done=True
print("mission complete")
print("final state:")
print("x:",x,"y:",y,"vx:",vx,"vy:",vy,"cos(A):",
cos_sq,"sin(A):",sin_sq,"omega:",omega,
"fuel level:",fuel_level)
print("final reward:",reward)
return np.array([x,y,vx,vy,cos_sq,sin_sq,
omega,fuel_level]),reward,True,
{"message":"mission complete"}
exit()
else:
reward-=100.
else:
reward-=100.
else:
reward-=100.
else:
reward-=100.
return np.array([x,y,vx,vy,cos_sq,sin_sq,
omega,fuel_level]),reward,True,{"message":"done"}
elif fuel_level <= getattr(self,"min_fuel",None):
done=True
return np.array([x,y,vx,vy,cos_sq,sin_sq,
omega,fuel_level]),-100.,True,{"message":"out of fuel"}
elif not -90.*np.pi/180.=getattr(self,"max_steps",None):
done=True
return np.array([x,y,vx,vy,cos_sq,sin_sq,
omega,fuel_level]),-100.,True,{"message":"max steps exceeded"}
else:
setattr(self,"x",x)
setattr(self,"y",y)
setattr(self,"vx",vx)
setattr(self,"vy",vy)
setattr(self,"cosA",cosA)
setattr(self,"sinA",sinA)
setattr(self,"omega",omega)
setattr(self,"fuel_level",fuel_level)
return np.array([x,y,vx,vy,cos_sq,sin_sq,
omega,fuel_level]),0.,False,{"message":"running"}
def render_mode_check(mode):
if mode not in ['human','rgb_array']:
raise NotImplementedError('only render modes human and rgb_array '
'are supported')
def render_human_setup():
viewer=None
world=None
screen=None
img_plane=None
lander=None
lndr_img=None
tfrm_lndr=None
tfrm_plt_lndr=None
trgt_lndr_img=None
trgt_plt_lndr=None
plume_img=None
tfrm_plume=None
tfrm_plt_plume=None
trgt_plume_img=None
trgt_plt_plume=None
def setup():
nonlocal viewer,img_plane,tfrm_lndr,tfrm_plt_lndr,trgt_lndr_img,trgt_plt_lndr,
plume_img,tfrm_plume,tfrm_plt_plume,trgt_plume_img,trgt_plt_plume
viewer=rendering.Viewer(600,400)
world=rendering.World()
screen=viewer.screen_width*viewer.screen_height*[255]
img_plane=rendering.make_polygon([(viewer.screen_width-.5),
viewer.screen_height-.5],
[(-viewer.screen_width+.5),
viewer.screen_height-.5],
[(viewer.screen_width-.5),
(-viewer.screen_height+.5)],
[(-viewer.screen_width+.5),
(-viewer.screen_height+.5)])
lander_img=rendering.make_polygon([(30./200),(-30./200)],
[(30./200),(30./200)],
[(-30./200),(30./200)],
[(-30./200),(-30./200)])
plume_img=rendering.make_circle(.25,.25)
tfrm_lndr=rendering.Transform()
tfrm_plt_lndr=rendering.Transform(rotation=np.pi/2.)
trgt_lndr_img=img_plane.add_image(lander_img,tfrm=tfrm_lndr,tfrm_plot=tfrm_plt_lndr)
tfrm_plume=rendering.Transform()
tfrm_plt_plume=rendering.Transform(rotation=np.pi/2.)
trgt_plume_img=img_plane.add_image(plume_img,tfrm=tfrm_plume,tfrm_plot=tfrm_plt_plume)
world.add_onetime(trgt_lndr_img)
world.add_onetime(trgt_plume_img)
viewer.set_bounds(-800,+800,-400,+400)
viewer.add_world(world)
viewer.render()
return viewer,img_plane,tfrm_lndr,tfrm_plt_lndr,trgt_lndr_img,trgt_plt_lndr,
plume_img,tfrm_plume,tfrm_plt_plume,trgt_plume_img,trgt_plt_plume,
setup
def