ctmakro

from __future__ import print_function
import numpy as np
import gym
from gym.spaces import Discrete, Box

# DQN for Reinforcement Learning
# by Qin Yongliang
# 2017 01 11

def continuous_actions(env):

ctmakro

from __future__ import print_function

# Deep Deterministic Policy Gradient Method
# David Silver et al.

# implemented in plain Keras, by Qin Yongliang
# 2017 01 13

'''
summary

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from __future__ import print_function

# Deep Deterministic Policy Gradient Method
# David Silver et al.

# implemented in plain Keras, by Qin Yongliang
# 2017 01 13

# heavily optimized for speed, lots of numpy flowed into tensorflow
# 2017 01 14

ctmakro

REM make sure you have miniconda installed

cd c:\
conda create -n osrl -c kidzik opensim git python=2.7

activate osrl
git clone https://github.com/stanfordnmbl/osim-rl
git clone https://github.com/ctmakro/stanford-osrl

pip install pyro4 pymsgbox

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import numpy as np

def trilaterate(points, distances):
    # ported from https://github.com/gheja/trilateration.js
    # points -> list of np arrays in the form of [[x, y, z], [x, y, z]
    # distances -> np array [r1, r2, r3]
    p1,p2,p3 = points
    r1,r2,r3 = distances
    def norm(v):
        return np.sqrt(np.sum(v**2))

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import numpy as np

def normalized(v):
    norm = np.sqrt(np.sum(v**2))
    return v / norm

# Trilateration to find the target point, given positions of 3 points, and distances(radiuses)
def trilaterate(points, distances):
    precision = 1e-3
    iteration = 1000

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# tacos=[[2.7647068559860712, -0.008816344965933138, 0.08419820920066164], [4.1482390698137275, 0.004365272677300839, 0.059974440683546126], [4.799411832657377, 0.012794506986796655, 0.07305748780077449], [2.395640662714245, 0.005136789959712116, 0.055235542912785984], [3.3942322982802495, 0.0034807961045444052, 0.058021672551073204], [4.556883550968967, 0.006043932006157158, 0.050294840880930024], [2.1072139294765933, 0.019171523718931313, 0.15339955024246338], [3.3087723737032717, 0.02240615211003253, 0.1343706158894652], [4.568062436714624, 0.01720806864461718, 0.07717739475940219]]
# psoas=[[1.0832688044403371, 1.0456262964311989], [1.0823437836374254, 0.8830063845929389], [0.9780701248134204, 1.016855183151065]]
test=None
test999=None

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# if input image is in range 0..1, please first multiply img by 255
# assume image is ndarray of shape [height, width, channels] where channels can be 1, 3 or 4
def imshow(img):
    import cv2
    import IPython
    _,ret = cv2.imencode('.jpg', img) 
    i = IPython.display.Image(data=ret)
    IPython.display.display(i)

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// return true if there is character in uart recv buffer, false otherwise.
int peek(){
	#include <xuartps_hw.h>
	#include <xparameters.h>
	return XUartPs_IsReceiveData(XPAR_PS7_UART_0_BASEADDR);
}

// it is recommended to use inbyte() to read a byte after peek() returned true.

ctmakro

/*
With an Arduino,
If you want to:
1) execute a function precisely every N microseconds (or precisely 1,000,000/N times every second), 
   REGARDLESS of the running time of that function
2) obtain a precision of +-4 microseconds with NO ACCUMULATION OF ERROR
3) use no 3rd party libraries

Then this is for you.
*/