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annotate python/prediction.py @ 489:000bddf84ad0

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corrected bugs in safety analysis
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Fri, 11 Apr 2014 17:47:38 -0400
parents 6464e4f0cc26
children 727e3c529519
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1 #! /usr/bin/env python
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2 '''Library for motion prediction methods'''
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3
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4 import moving
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5 import math
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6 import random
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7
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8 class PredictedTrajectory:
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9 '''Class for predicted trajectories with lazy evaluation
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10 if the predicted position has not been already computed, compute it
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11
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12 it should also have a probability'''
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13
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14 def __init__(self):
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15 self.probability = 0.
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16 self.predictedPositions = {}
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17 self.predictedSpeedOrientations = {}
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18 #self.collisionPoints = {}
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19 #self.crossingZones = {}
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20
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21 def predictPosition(self, nTimeSteps):
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22 if nTimeSteps > 0 and not nTimeSteps in self.predictedPositions.keys():
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23 self.predictPosition(nTimeSteps-1)
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24 self.predictedPositions[nTimeSteps], self.predictedSpeedOrientations[nTimeSteps] = moving.predictPosition(self.predictedPositions[nTimeSteps-1], self.predictedSpeedOrientations[nTimeSteps-1], self.getControl(), self.maxSpeed)
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25 return self.predictedPositions[nTimeSteps]
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26
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27 def getPredictedTrajectory(self):
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28 return moving.Trajectory.fromPointList(self.predictedPositions.values())
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29
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30 def getPredictedSpeeds(self):
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31 return [so.norm for so in self.predictedSpeedOrientations.values()]
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32
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33 def draw(self, options = '', withOrigin = False, timeStep = 1, **kwargs):
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34 self.getPredictedTrajectory().draw(options, withOrigin, timeStep, **kwargs)
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35
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36 class PredictedTrajectoryConstant(PredictedTrajectory):
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37 '''Predicted trajectory at constant speed or acceleration
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38 TODO generalize by passing a series of velocities/accelerations'''
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39
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40 def __init__(self, initialPosition, initialVelocity, control = moving.NormAngle(0,0), probability = 1., maxSpeed = None):
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41 self.control = control
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42 self.maxSpeed = maxSpeed
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43 self.probability = probability
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44 self.predictedPositions = {0: initialPosition}
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45 self.predictedSpeedOrientations = {0: moving.NormAngle.fromPoint(initialVelocity)}
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46
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47 def getControl(self):
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48 return self.control
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49
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50 class PredictedTrajectoryPrototype(PredictedTrajectory):
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51 '''Predicted trajectory that follows a prototype trajectory
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52 The prototype is in the format of a moving.Trajectory: it could be
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53 1. an observed trajectory (extracted from video)
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54 2. a generic polyline (eg the road centerline) that a vehicle is supposed to follow
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55
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56 Prediction can be done
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57 1. at constant speed (the instantaneous user speed)
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58 2. following the trajectory path, at the speed of the user
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59 (applying a constant ratio equal
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60 to the ratio of the user instantaneous speed and the trajectory closest speed)'''
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61
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62 def __init__(self, initialPosition, initialVelocity, prototypeTrajectory, constantSpeed = True, probability = 1.):
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63 self.prototypeTrajectory = prototypeTrajectory
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64 self.constantSpeed = constantSpeed
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65 self.probability = probability
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66 self.predictedPositions = {0: initialPosition}
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67 self.predictedSpeedOrientations = {0: moving.NormAngle.fromPoint(initialVelocity)}
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68
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69 def predictPosition(self, nTimeSteps):
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70 if nTimeSteps > 0 and not nTimeSteps in self.predictedPositions.keys():
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71 if constantSpeed:
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72 # calculate cumulative distance
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73 pass
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74 else: # see c++ code, calculate ratio
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75 pass
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76 return self.predictedPositions[nTimeSteps]
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77
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78 class PredictedTrajectoryRandomControl(PredictedTrajectory):
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79 '''Random vehicle control: suitable for normal adaptation'''
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80 def __init__(self, initialPosition, initialVelocity, accelerationDistribution, steeringDistribution, probability = 1., maxSpeed = None):
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81 '''Constructor
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82 accelerationDistribution and steeringDistribution are distributions
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83 that return random numbers drawn from them'''
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84 self.accelerationDistribution = accelerationDistribution
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85 self.steeringDistribution = steeringDistribution
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86 self.maxSpeed = maxSpeed
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87 self.probability = probability
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88 self.predictedPositions = {0: initialPosition}
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89 self.predictedSpeedOrientations = {0: moving.NormAngle.fromPoint(initialVelocity)}
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90
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91 def getControl(self):
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92 return moving.NormAngle(self.accelerationDistribution(),self.steeringDistribution())
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93
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94 class SafetyPoint(moving.Point):
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95 '''Can represent a collision point or crossing zone
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96 with respective safety indicator, TTC or pPET'''
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97 def __init__(self, p, probability = 1., indicator = -1):
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98 self.x = p.x
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99 self.y = p.y
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100 self.probability = probability
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101 self.indicator = indicator
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102
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103 def __str__(self):
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104 return '{0} {1} {2} {3}'.format(self.x, self.y, self.probability, self.indicator)
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105
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106 @staticmethod
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107 def save(out, points, predictionInstant, objNum1, objNum2):
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108 for p in points:
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109 out.write('{0} {1} {2} {3}\n'.format(objNum1, objNum2, predictionInstant, p))
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110
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111 @staticmethod
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112 def computeExpectedIndicator(points):
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113 from numpy import sum
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114 return sum([p.indicator*p.probability for p in points])/sum([p.probability for p in points])
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115
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116 def computeCollisionTime(predictedTrajectory1, predictedTrajectory2, collisionDistanceThreshold, timeHorizon):
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117 '''Computes the first instant at which two predicted trajectories are within some distance threshold'''
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118 t = 1
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119 p1 = predictedTrajectory1.predictPosition(t)
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120 p2 = predictedTrajectory2.predictPosition(t)
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121 while t <= timeHorizon and (p1-p2).norm2() > collisionDistanceThreshold:
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122 p1 = predictedTrajectory1.predictPosition(t)
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123 p2 = predictedTrajectory2.predictPosition(t)
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124 t += 1
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125 return t, p1, p2
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126
271
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127 class PredictionParameters:
266
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128 def __init__(self, name, maxSpeed):
257
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129 self.name = name
266
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130 self.maxSpeed = maxSpeed
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131
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132 def __str__(self):
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133 return '{0} {1}'.format(self.name, self.maxSpeed)
257
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134
358
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135 def generatePredictedTrajectories(self, obj, instant):
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136 return []
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137
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138 def computeCrossingsCollisionsAtInstant(self, currentInstant, obj1, obj2, collisionDistanceThreshold, timeHorizon, computeCZ = False, debug = False):
359
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139 '''returns the lists of collision points and crossing zones'''
358
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140 predictedTrajectories1 = self.generatePredictedTrajectories(obj1, currentInstant)
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141 predictedTrajectories2 = self.generatePredictedTrajectories(obj2, currentInstant)
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142
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143 collisionPoints = []
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144 crossingZones = []
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145 for et1 in predictedTrajectories1:
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146 for et2 in predictedTrajectories2:
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147 t, p1, p2 = computeCollisionTime(et1, et2, collisionDistanceThreshold, timeHorizon)
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148
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149 if t <= timeHorizon:
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150 collisionPoints.append(SafetyPoint((p1+p2).multiply(0.5), et1.probability*et2.probability, t))
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151 elif computeCZ: # check if there is a crossing zone
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152 # TODO? zone should be around the points at which the traj are the closest
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153 # look for CZ at different times, otherwise it would be a collision
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154 # an approximation would be to look for close points at different times, ie the complementary of collision points
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155 cz = None
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156 t1 = 0
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157 while not cz and t1 < timeHorizon: # t1 <= timeHorizon-1
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158 t2 = 0
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159 while not cz and t2 < timeHorizon:
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160 #if (et1.predictPosition(t1)-et2.predictPosition(t2)).norm2() < collisionDistanceThreshold:
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161 # cz = (et1.predictPosition(t1)+et2.predictPosition(t2)).multiply(0.5)
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162 cz = moving.segmentIntersection(et1.predictPosition(t1), et1.predictPosition(t1+1), et2.predictPosition(t2), et2.predictPosition(t2+1))
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163 if cz:
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164 crossingZones.append(SafetyPoint(cz, et1.probability*et2.probability, abs(t1-t2)))
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165 t2 += 1
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166 t1 += 1
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167
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168 if debug:
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169 from matplotlib.pyplot import figure, axis, title
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170 figure()
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171 for et in predictedTrajectories1:
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172 et.predictPosition(timeHorizon)
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173 et.draw('rx')
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174
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175 for et in predictedTrajectories2:
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176 et.predictPosition(timeHorizon)
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177 et.draw('bx')
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178 obj1.draw('r')
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179 obj2.draw('b')
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180 title('instant {0}'.format(currentInstant))
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181 axis('equal')
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182
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183 return collisionPoints, crossingZones
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184
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185 def computeCrossingsCollisions(self, obj1, obj2, collisionDistanceThreshold, timeHorizon, computeCZ = False, debug = False, timeInterval = None):
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186 '''Computes all crossing and collision points at each common instant for two road users. '''
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187 collisionPoints={}
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188 crossingZones={}
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189 if timeInterval:
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190 commonTimeInterval = timeInterval
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191 else:
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192 commonTimeInterval = obj1.commonTimeInterval(obj2)
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193 for i in list(commonTimeInterval)[:-1]: # do not look at the 1 last position/velocities, often with errors
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diff changeset
194 collisionPoints[i], crossingZones[i] = self.computeCrossingsCollisionsAtInstant(i, obj1, obj2, collisionDistanceThreshold, timeHorizon, computeCZ, debug)
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195
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196 return collisionPoints, crossingZones
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197
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198 def computeCollisionProbability(self, obj1, obj2, collisionDistanceThreshold, timeHorizon, debug = False, timeInterval = None):
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199 '''Computes only collision probabilities
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200 Returns for each instant the collision probability and number of samples drawn'''
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201 collisionProbabilities = {}
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diff changeset
202 if timeInterval:
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diff changeset
203 commonTimeInterval = timeInterval
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204 else:
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205 commonTimeInterval = obj1.commonTimeInterval(obj2)
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206 for i in list(commonTimeInterval)[:-1]:
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207 nCollisions = 0
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208 print(obj1.num, obj2.num, i)
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209 predictedTrajectories1 = self.generatePredictedTrajectories(obj1, i)
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diff changeset
210 predictedTrajectories2 = self.generatePredictedTrajectories(obj2, i)
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diff changeset
211 for et1 in predictedTrajectories1:
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diff changeset
212 for et2 in predictedTrajectories2:
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diff changeset
213 t, p1, p2 = computeCollisionTime(et1, et2, collisionDistanceThreshold, timeHorizon)
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diff changeset
214 if t <= timeHorizon:
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diff changeset
215 nCollisions += 1
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diff changeset
216 # take into account probabilities ??
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diff changeset
217 nSamples = float(len(predictedTrajectories1)*len(predictedTrajectories2))
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diff changeset
218 collisionProbabilities[i] = [nSamples, float(nCollisions)/nSamples]
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diff changeset
219
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diff changeset
220 if debug:
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parents: 357
diff changeset
221 from matplotlib.pyplot import figure, axis, title
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diff changeset
222 figure()
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diff changeset
223 for et in predictedTrajectories1:
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diff changeset
224 et.predictPosition(timeHorizon)
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diff changeset
225 et.draw('rx')
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diff changeset
226
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diff changeset
227 for et in predictedTrajectories2:
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diff changeset
228 et.predictPosition(timeHorizon)
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diff changeset
229 et.draw('bx')
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parents: 357
diff changeset
230 obj1.draw('r')
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diff changeset
231 obj2.draw('b')
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diff changeset
232 title('instant {0}'.format(i))
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diff changeset
233 axis('equal')
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parents: 357
diff changeset
234
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235 return collisionProbabilities
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diff changeset
236
271
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237 class ConstantPredictionParameters(PredictionParameters):
268
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238 def __init__(self, maxSpeed):
271
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diff changeset
239 PredictionParameters.__init__(self, 'constant velocity', maxSpeed)
268
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diff changeset
240
271
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241 def generatePredictedTrajectories(self, obj, instant):
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diff changeset
242 return [PredictedTrajectoryConstant(obj.getPositionAtInstant(instant), obj.getVelocityAtInstant(instant),
268
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parents: 267
diff changeset
243 maxSpeed = self.maxSpeed)]
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diff changeset
244
271
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245 class NormalAdaptationPredictionParameters(PredictionParameters):
460
55b424d98b68 change of interface, distributions are now passed to the prediction paramters constructors if needed
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246 def __init__(self, maxSpeed, nPredictedTrajectories, accelerationDistribution, steeringDistribution, useFeatures = False):
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247 '''An example of acceleration and steering distributions is
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248 lambda: random.triangular(-self.maxAcceleration, self.maxAcceleration, 0.)
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249 '''
352
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250 if useFeatures:
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251 name = 'point set normal adaptation'
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252 else:
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253 name = 'normal adaptation'
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254 PredictionParameters.__init__(self, name, maxSpeed)
271
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diff changeset
255 self.nPredictedTrajectories = nPredictedTrajectories
352
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diff changeset
256 self.useFeatures = useFeatures
460
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257 self.accelerationDistribution = accelerationDistribution
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258 self.steeringDistribution = steeringDistribution
268
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259
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diff changeset
260 def __str__(self):
271
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diff changeset
261 return PredictionParameters.__str__(self)+' {0} {1} {2}'.format(self.nPredictedTrajectories,
352
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diff changeset
262 self.maxAcceleration,
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diff changeset
263 self.maxSteering)
268
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diff changeset
264
271
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diff changeset
265 def generatePredictedTrajectories(self, obj, instant):
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diff changeset
266 predictedTrajectories = []
352
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diff changeset
267 if self.useFeatures:
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diff changeset
268 features = [f for f in obj.features if f.existsAtInstant(instant)]
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269 positions = [f.getPositionAtInstant(instant) for f in features]
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270 velocities = [f.getVelocityAtInstant(instant) for f in features]
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271 else:
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272 positions = [obj.getPositionAtInstant(instant)]
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273 velocities = [obj.getVelocityAtInstant(instant)]
271
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274 for i in xrange(self.nPredictedTrajectories):
352
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diff changeset
275 for initialPosition,initialVelocity in zip(positions, velocities):
466
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parents: 464
diff changeset
276 predictedTrajectories.append(PredictedTrajectoryRandomControl(initialPosition,
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parents: 464
diff changeset
277 initialVelocity,
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diff changeset
278 self.accelerationDistribution,
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279 self.steeringDistribution,
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diff changeset
280 maxSpeed = self.maxSpeed))
271
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diff changeset
281 return predictedTrajectories
268
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diff changeset
282
271
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diff changeset
283 class PointSetPredictionParameters(PredictionParameters):
269
a9988971aac8 removed legacy code + tweaks
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parents: 268
diff changeset
284 # todo generate several trajectories with normal adaptatoins from each position (feature)
489
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diff changeset
285 def __init__(self, maxSpeed):
271
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diff changeset
286 PredictionParameters.__init__(self, 'point set', maxSpeed)
489
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parents: 484
diff changeset
287 #self.nPredictedTrajectories = nPredictedTrajectories
268
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diff changeset
288
271
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diff changeset
289 def generatePredictedTrajectories(self, obj, instant):
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diff changeset
290 predictedTrajectories = []
268
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diff changeset
291 features = [f for f in obj.features if f.existsAtInstant(instant)]
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parents: 267
diff changeset
292 positions = [f.getPositionAtInstant(instant) for f in features]
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parents: 267
diff changeset
293 velocities = [f.getVelocityAtInstant(instant) for f in features]
489
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parents: 484
diff changeset
294 #for i in xrange(self.nPredictedTrajectories):
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diff changeset
295 for initialPosition,initialVelocity in zip(positions, velocities):
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diff changeset
296 predictedTrajectories.append(PredictedTrajectoryConstant(initialPosition, initialVelocity,
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parents: 484
diff changeset
297 maxSpeed = self.maxSpeed))
271
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diff changeset
298 return predictedTrajectories
268
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diff changeset
299
271
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diff changeset
300 class EvasiveActionPredictionParameters(PredictionParameters):
460
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diff changeset
301 def __init__(self, maxSpeed, nPredictedTrajectories, accelerationDistribution, steeringDistribution, useFeatures = False):
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diff changeset
302 '''Suggested acceleration distribution may not be symmetric, eg
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diff changeset
303 lambda: random.triangular(self.minAcceleration, self.maxAcceleration, 0.)'''
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diff changeset
304
268
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diff changeset
305 if useFeatures:
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diff changeset
306 name = 'point set evasive action'
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diff changeset
307 else:
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diff changeset
308 name = 'evasive action'
271
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diff changeset
309 PredictionParameters.__init__(self, name, maxSpeed)
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diff changeset
310 self.nPredictedTrajectories = nPredictedTrajectories
268
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diff changeset
311 self.useFeatures = useFeatures
460
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diff changeset
312 self.accelerationDistribution = accelerationDistribution
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diff changeset
313 self.steeringDistribution = steeringDistribution
268
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parents: 267
diff changeset
314
0c0b92f621f6 reorganized to compute evasive action for multiple positions
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diff changeset
315 def __str__(self):
271
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diff changeset
316 return PredictionParameters.__str__(self)+' {0} {1} {2} {3}'.format(self.nPredictedTrajectories, self.minAcceleration, self.maxAcceleration, self.maxSteering)
268
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diff changeset
317
271
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diff changeset
318 def generatePredictedTrajectories(self, obj, instant):
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diff changeset
319 predictedTrajectories = []
268
0c0b92f621f6 reorganized to compute evasive action for multiple positions
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 267
diff changeset
320 if self.useFeatures:
267
32e88b513f5c added code to compute probability of collision
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parents: 266
diff changeset
321 features = [f for f in obj.features if f.existsAtInstant(instant)]
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 266
diff changeset
322 positions = [f.getPositionAtInstant(instant) for f in features]
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parents: 266
diff changeset
323 velocities = [f.getVelocityAtInstant(instant) for f in features]
268
0c0b92f621f6 reorganized to compute evasive action for multiple positions
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diff changeset
324 else:
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diff changeset
325 positions = [obj.getPositionAtInstant(instant)]
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diff changeset
326 velocities = [obj.getVelocityAtInstant(instant)]
271
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diff changeset
327 for i in xrange(self.nPredictedTrajectories):
267
32e88b513f5c added code to compute probability of collision
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diff changeset
328 for initialPosition,initialVelocity in zip(positions, velocities):
271
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diff changeset
329 predictedTrajectories.append(PredictedTrajectoryConstant(initialPosition,
350
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 336
diff changeset
330 initialVelocity,
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 336
diff changeset
331 moving.NormAngle(self.accelerationDistribution(),
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parents: 336
diff changeset
332 self.steeringDistribution()),
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parents: 336
diff changeset
333 maxSpeed = self.maxSpeed))
271
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diff changeset
334 return predictedTrajectories
257
9281878ff19e untested collision/crossing computation
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 256
diff changeset
335
359
619ae9a9a788 implemented prediction method at constant velocity with direct intersection computation
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diff changeset
336
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diff changeset
337 class CVDirectPredictionParameters(PredictionParameters):
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diff changeset
338 '''Prediction parameters of prediction at constant velocity
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diff changeset
339 using direct computation of the intersecting point'''
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
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diff changeset
340
387
91679eb2ff2c cleaning up safety analysis and the new traditional constant velocity method
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diff changeset
341 def __init__(self):
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parents: 359
diff changeset
342 PredictionParameters.__init__(self, 'constant velocity (direct computation)', None)
359
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diff changeset
343
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parents: 358
diff changeset
344 def computeCrossingsCollisionsAtInstant(self, currentInstant, obj1, obj2, collisionDistanceThreshold, timeHorizon, computeCZ = False, debug = False):
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diff changeset
345 collisionPoints = []
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diff changeset
346 crossingZones = []
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diff changeset
347
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diff changeset
348 p1 = obj1.getPositionAtInstant(currentInstant)
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parents: 358
diff changeset
349 p2 = obj2.getPositionAtInstant(currentInstant)
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parents: 358
diff changeset
350 if (p1-p2).norm2() <= collisionDistanceThreshold:
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parents: 358
diff changeset
351 collisionPoints = [SafetyPoint((p1+p1).multiply(0.5), 1., 0.)]
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parents: 358
diff changeset
352 else:
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diff changeset
353 v1 = obj1.getVelocityAtInstant(currentInstant)
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parents: 358
diff changeset
354 v2 = obj2.getVelocityAtInstant(currentInstant)
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parents: 358
diff changeset
355 intersection = moving.intersection(p1, p2, v1, v2)
258
d90be3c02267 reasonably efficient computation of collision points and crossing zones
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 257
diff changeset
356
359
619ae9a9a788 implemented prediction method at constant velocity with direct intersection computation
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 358
diff changeset
357 if intersection != None:
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 358
diff changeset
358 dp1 = intersection-p1
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359 dp2 = intersection-p2
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360 if moving.Point.dot(dp1, v1) > 0 and moving.Point.dot(dp2, v2) > 0: # if the road users are moving towards the intersection
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361 dist1 = dp1.norm2()
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362 dist2 = dp2.norm2()
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363 s1 = v1.norm2()
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364 s2 = v2.norm2()
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365 halfCollisionDistanceThreshold = collisionDistanceThreshold/2.
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366 timeInterval1 = moving.TimeInterval(max(0,dist1-halfCollisionDistanceThreshold)/s1, (dist1+halfCollisionDistanceThreshold)/s1)
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367 timeInterval2 = moving.TimeInterval(max(0,dist2-halfCollisionDistanceThreshold)/s2, (dist2+halfCollisionDistanceThreshold)/s2)
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368 collisionTimeInterval = moving.TimeInterval.intersection(timeInterval1, timeInterval2)
387
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369 if computeCZ and collisionTimeInterval.empty():
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370 crossingZones = [SafetyPoint(intersection, 1., timeInterval1.distance(timeInterval2))]
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371 else:
359
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372 collisionPoints = [SafetyPoint(intersection, 1., collisionTimeInterval.center())]
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373
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374 if debug and intersection!= None:
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375 from matplotlib.pyplot import plot, figure, axis, title
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376 figure()
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377 plot([p1.x, intersection.x], [p1.y, intersection.y], 'r')
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378 plot([p2.x, intersection.x], [p2.y, intersection.y], 'b')
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379 intersection.draw()
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380 obj1.draw('r')
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381 obj2.draw('b')
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382 title('instant {0}'.format(currentInstant))
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383 axis('equal')
289
e56c34c1ebac refactored and commented functions (saving data is now outside of the computation functions)
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384
359
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385 return collisionPoints, crossingZones
260
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386
464
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387 class CVExactPredictionParameters(PredictionParameters):
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388 '''Prediction parameters of prediction at constant velocity
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389 using direct computation of the intersecting point (solving for the equation'''
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390
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391 def __init__(self):
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392 PredictionParameters.__init__(self, 'constant velocity (direct exact computation)', None)
359
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393
464
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394 def computeCrossingsCollisionsAtInstant(self, currentInstant, obj1, obj2, collisionDistanceThreshold, timeHorizon, computeCZ = False, debug = False):
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395 'TODO add collision point coordinates, compute pPET'
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396 #collisionPoints = []
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397 #crossingZones = []
269
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398
464
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399 p1 = obj1.getPositionAtInstant(currentInstant)
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400 p2 = obj2.getPositionAtInstant(currentInstant)
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401 v1 = obj1.getVelocityAtInstant(currentInstant)
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402 v2 = obj2.getVelocityAtInstant(currentInstant)
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diff changeset
403 intersection = moving.intersection(p1, p2, v1, v2)
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404
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405 if intersection != None:
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406 ttc = moving.Point.timeToCollision(p1, p2, v1, v2, collisionDistanceThreshold)
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407 if ttc:
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408 return [SafetyPoint(intersection, 1., ttc)], [] # (p1+v1.multiply(ttc)+p2+v2.multiply(ttc)).multiply(0.5)
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409 else:
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410 return [],[]
260
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411
357
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diff changeset
412 ####
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diff changeset
413 # Other Methods
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diff changeset
414 ####
e5fe0e6d48a1 corrected bug computing TTC (resp. pPET) if there is no collision point (resp. crossing zone)
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parents: 355
diff changeset
415
e5fe0e6d48a1 corrected bug computing TTC (resp. pPET) if there is no collision point (resp. crossing zone)
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parents: 355
diff changeset
416
e5fe0e6d48a1 corrected bug computing TTC (resp. pPET) if there is no collision point (resp. crossing zone)
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parents: 355
diff changeset
417
e5fe0e6d48a1 corrected bug computing TTC (resp. pPET) if there is no collision point (resp. crossing zone)
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diff changeset
418
243
e0988a8ace0c started adapting and moving to other modules Mohamed's work
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diff changeset
419
255
13ec22bec5d4 corrected typos and bugs and added a test
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diff changeset
420 if __name__ == "__main__":
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421 import doctest
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422 import unittest
271
bbd9c09e6869 changed the names to prediction methods and predicted trajectories
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diff changeset
423 suite = doctest.DocFileSuite('tests/prediction.txt')
255
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diff changeset
424 #suite = doctest.DocTestSuite()
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425 unittest.TextTestRunner().run(suite)
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diff changeset
426 #doctest.testmod()
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diff changeset
427 #doctest.testfile("example.txt")
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diff changeset
428