scturtle

icon_gravity E
geometry 5x1-0+0
max_geometry 5x1-0+0
background "#000000"
icon_size 20
kludges force_icons_size
skip_taskbar

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from __future__ import print_function
import numpy as np
from scipy.stats import norm
import matplotlib.pyplot as plt
import tensorflow as tf
import tensorflow.contrib.layers as tfl
import tensorflow.contrib.framework as tff

def minibatch(features):
    size_a, size_b, size_c = features.get_shape()[1], 3, 3

scturtle

import numpy as np
from scipy.stats import norm
import matplotlib.pyplot as plt
import matplotlib.animation as animation
from matplotlib.patches import Arrow, Circle

fig = plt.figure("pf", figsize=(7., 7.))
ax = fig.gca()

landmarks = [[20., 20.], [20., 50.], [20., 80.],

scturtle

from __future__ import print_function
import string
import zipfile
import numpy as np
import tensorflow as tf
import tensorflow.contrib.layers as layers

class BatchGenerator:

    def __init__(self, text, vocabulary, batch_size, num_unrollings):

scturtle

import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from transforms3d import euler

fig = plt.figure()
ax = fig.gca(projection='3d')

p1 = np.zeros((3, 100))
p1[0, :] = np.linspace(1, 3, 100)

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import numpy as np
import numpy.linalg as npl
from easydict import EasyDict

opts = EasyDict(dict(max_iter=10, eps1=1e-8, eps2=1e-8))

def fJ(x, p, y=0):
    f = p[0] * np.exp(- (x - p[1]) ** 2 / (2 * p[2] ** 2))
    J = np.empty((p.size, x.size), dtype=np.float)
    J[0, :] = f / p[0]

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import numpy as np
from numpy.linalg import inv
import matplotlib.pyplot as plt

delta_t = 0.1
t = np.arange(0, 5, delta_t)
n = len(t)
# 加速度
g = 10
# 真实位置

scturtle

scturtle

import os
import bottle

ip = '233.233.233.233'
x = 51.505494
y = -0.12369
add = 0.0001
p1 = (55, 770)
p2 = (55, 820)

scturtle

fn horspool<T: Eq+Hash>(haystack: &[T], needle: &[T]) -> isize {
    let hlen = haystack.len();
    let nlen = needle.len();
    if nlen == 0 { return 0 }
    // jump table
    let jmp: HashMap<&T, usize> = needle.iter().zip((1..nlen).rev()).collect();
    let last = nlen - 1;
    let mut j = 0;
    while j <= hlen - nlen {
        // search backward