def update_weights(self, reward, state, new_state, action, new_action):
""" Update the agents weights after a non-terminal step. """
x = tiles.tiles(self.iht, self.no_of_tiles, np.append(state, action)*self.normalise)
new_x = tiles.tiles(self.iht, self.no_of_tiles, np.append(new_state, new_action)*self.normalise)
q = sum([self.weights[x[i]][i] for i in range(self.no_of_tiles)])
new_q = sum([self.weights[new_x[i]][i] for i in range(self.no_of_tiles)])
for i in range(self.no_of_tiles):
self.weights[x[i]][i] += self.training_rate*(reward + new_q - q)
def runTest(seed=65597):
import tiles
i = 5
j = 6
print 'seed: ' + str(seed)
tiles.setseed(seed)
print tiles.tiles(10, 2048, [i * 0.5, j * 0.5])
def runTest(seed = 65597):
import tiles
i=5
j=6
print 'seed: '+str(seed)
tiles.setseed(seed)
print tiles.tiles(10, 2048, [i*0.5, j*0.5])
def calcTiledata(self, numtilings, memct, floats, ints=[]):
samet = []
sametd = []
sametbd = []
sametdm = []
sametl = []
samete = []
samets = []
t = tiles.tiles(numtilings, memct, floats, ints)
tsx = fancytiles.stripetiles(numtilings, memct, [floats[0]], None,
ints)
tsy = fancytiles.stripetiles(numtilings, memct, [floats[1]], None,
ints)
td = fancytiles.diagonaltiles(numtilings, memct, floats, None, ints)
tbd = fancytiles.backdiagonaltiles(numtilings, memct, floats, None,
ints)
tdm = fancytiles.diamondtiles(numtilings, memct, floats, None, ints)
tl = fancytiles.logtiles(numtilings, memct, floats, ints)
te = fancytiles.exptiles(numtilings, memct, floats, ints)
total = int((self.end - self.start) * self.intervals)
for i in range(total):
for j in range(total):
x = float(i) / self.intervals + self.start
y = float(j) / self.intervals + self.start
newfloats = [x, y]
if tiles.tiles(numtilings, memct, newfloats, ints) == t:
samet.append(newfloats)
if fancytiles.stripetiles(numtilings, memct, [x], None,
ints) == tsx or \
fancytiles.stripetiles(numtilings, memct, [y],
None, ints) == tsy:
samets.append(newfloats)
if fancytiles.diagonaltiles(numtilings, memct, newfloats, None,
ints) == td:
sametd.append(newfloats)
if fancytiles.backdiagonaltiles(numtilings, memct, newfloats,
None, ints) == tbd:
sametbd.append(newfloats)
if fancytiles.diamondtiles(numtilings, memct, newfloats, None,
ints) == tdm:
sametdm.append(newfloats)
if fancytiles.logtiles(numtilings, memct, newfloats,
ints) == tl:
sametl.append(newfloats)
if fancytiles.exptiles(numtilings, memct, newfloats,
ints) == te:
samete.append(newfloats)
data = [samet, samets, sametd, sametbd, sametdm, sametl, samete]
return data
def stripetiles(numtilings, memctable, floats, widths=None, ints=[]):
"returns tiles in the shape of stripes (scaled by widths), a set for each dimension in floats"
floats = scalefloats(floats, widths)
# should another int be added here for each dimension if there is more than one?
return reduce(operator.add,
[tiles.tiles(numtilings, memctable, [f], ints) for f in
floats])
def act(self, state, reward):
""" Epsilon-greedy policy. """
x0 = tiles.tiles(self.iht, self.no_of_tiles, np.append(state, 0)*self.normalise)
x1 = tiles.tiles(self.iht, self.no_of_tiles, np.append(state, 1)*self.normalise)
a0 = sum([self.weights[x0[i]][i] for i in range(self.no_of_tiles)])
a1 = sum([self.weights[x1[i]][i] for i in range(self.no_of_tiles)])
if np.random.rand() < 0.9:
if a0 > a1:
action = 0
else:
action = 1
else:
if a1 > a0:
action = 0
else:
action = 1
return action
def runit2(num=10, ct=2048, numt=1):
for i in xrange(num):
for j in xrange(num):
t = tiles.tiles(
numt, ct,
[i * 0.5, j * 0.5,
float(i + j) / 2,
float(i - j) / 2], [i, j])
def fancytiles(numtilings, floats, tileshape="square", tilesize="uniform", \
tilewidths=None, memctable=2048, ints=[]):
"""Does appropriate manipulations to get special shaped or sized tiles.
Tileshapes are 'square' - square or rectangular tiles (the default),
'stripe' - stripes for each dimension, and
'diagonal' - diagonal stripes for each pair of dimensions
'backdiagonal' diagonal stripes the other way
'alldiagonal' - diagonal stripes for all dimensions at once
'allbackdiagonal' - diagonal stripes the other way for all dimensions at once
'diamond' - diagonal stripes and back diagonal stripes are applied
Tilesizes are 'uniform' - all tiles are the same size (the default),
'log' - tile sizes vary logarithmically from small to large, and
'exp' - tile sizes vary exponentially from large to small
Scaling is handled first if tilewidths is set - there should be a width for every
element of the float vector. If you don't wish to apply scaling to the floats,
tilewidths may be set to None, or to a vector of 1's the same length as floats.
memctable, numtilings and ints are the same as for the regular tiles routine.
Note that you may get back more tiles than numtilings - for stripes or diagonal stripes
you may get a larger number of tiles back.
"""
if tilewidths != None: # apply scaling, if desired
floats = scalefloats(floats, tilewidths)
if tilesize == "log": # logarithmic sized tiles
floats = logfloats(floats)
elif tilesize == "exp": # exponential sized tiles
floats = expfloats(floats)
if tileshape == "stripe": # stripes - do one set for each variable in floats
return reduce(operator.add, [tiles.tiles(numtilings, memctable, [f], ints) for f in floats])
elif tileshape == "diagonal": # diagonal stripes for every pair of dimensions
flist = floats[:]
tlist = []
while len(flist) > 1:
current = flist[0]
for next in flist[1:]:
tlist.extend(tiles.tiles(numtilings, memctable, diagonalstripe([current, next]), ints))
flist = flist[1:]
return tlist
elif tileshape == "backdiagonal": # diagonal stripes for every pair of dimensions
flist = floats[:]
tlist = []
while len(flist) > 1:
current = flist[0]
for next in flist[1:]:
tlist.extend(tiles.tiles(numtilings, memctable, backdiagonalstripe([current, next]), ints))
flist = flist[1:]
return tlist
elif tileshape == "alldiagonal": # diagonal stripe through all dimensions at once - no different than diag?
return tiles.tiles(numtilings, memctable, diagonalfloats(floats), ints)
elif tileshape == "allbackdiagonal": # diagonal stripe through all dimensions at once
return tiles.tiles(numtilings, memctable, backdiagonalfloats(floats), ints)
elif tileshape == "diamond": # diamond shaped tiles
floats1 = diagonalfloats(floats)
floats2 = backdiagonalfloats(floats)
return tiles.tiles(numtilings, memctable, floats1+floats2, ints)
else: # square/rectangular - do the regular tiles
return tiles.tiles(numtilings, memctable, floats, ints)
def getfeatvec(res, normS):
## pure pythong tilecoding
# activetiles = [-1]*numTilings
# tilesOut = tilecode(numTilings, res, activetiles, normS)
## cython tilecoder is faster
scalednormS = [x * res for x in normS]
tilesOut = tiles.tiles(numTilings, np.power(res, numFeatures)*numTilings, scalednormS)
return tilesOut
def transform(self, state):
# Normalize values to range from [0, 1] for use in transformations
position, velocity = state
position = (position + 1.2) / 1.8
velocity = (velocity + 0.07) / 0.14
assert 0 <= position <= 1
assert 0 <= velocity <= 1
position *= 2
velocity *= 2
if self.mode == "tile":
if self.iht is None:
self.iht = IHT(self.state_space)
tiling = tiles(self.iht, 64, [position, velocity], [0]) + \
tiles(self.iht, 64, [position], [1]) + \
tiles(self.iht, 64, [velocity], [2])
return {index: 1 for index in tiling}
elif self.mode == "raw":
return dict(enumerate(state))
else:
raise Error("Invalid environment mode. Must be tile or raw")
def calcTiledata(self, numtilings, memct, floats, ints=[]):
samet = []
sametd = []
sametbd = []
sametdm = []
sametl = []
samete = []
samets = []
t = tiles.tiles(numtilings, memct, floats, ints)
tsx = fancytiles.stripetiles(numtilings, memct, [floats[0]], None, ints)
tsy = fancytiles.stripetiles(numtilings, memct, [floats[1]], None, ints)
td = fancytiles.diagonaltiles(numtilings, memct, floats, None, ints)
tbd = fancytiles.backdiagonaltiles(numtilings, memct, floats, None, ints)
tdm = fancytiles.diamondtiles(numtilings, memct, floats, None, ints)
tl = fancytiles.logtiles(numtilings, memct, floats, ints)
te = fancytiles.exptiles(numtilings, memct, floats, ints)
total = int((self.end - self.start) * self.intervals)
for i in xrange(total):
for j in xrange(total):
x = float(i)/self.intervals + self.start
y = float(j)/self.intervals + self.start
newfloats = [x, y]
if tiles.tiles(numtilings, memct, newfloats, ints) == t:
samet.append(newfloats)
if fancytiles.stripetiles(numtilings, memct, [x], None, ints) == tsx or \
fancytiles.stripetiles(numtilings, memct, [y], None, ints) == tsy:
samets.append(newfloats)
if fancytiles.diagonaltiles(numtilings, memct, newfloats, None, ints) == td:
sametd.append(newfloats)
if fancytiles.backdiagonaltiles(numtilings, memct, newfloats, None, ints) == tbd:
sametbd.append(newfloats)
if fancytiles.diamondtiles(numtilings, memct, newfloats, None, ints) == tdm:
sametdm.append(newfloats)
if fancytiles.logtiles(numtilings, memct, newfloats, ints) == tl:
sametl.append(newfloats)
if fancytiles.exptiles(numtilings, memct, newfloats, ints) == te:
samete.append(newfloats)
data = [samet, samets, sametd, sametbd, sametdm, sametl, samete]
return data
def loadFeatures(self, stateVars, featureVector):
# loadtiles(featureVector, 0, self.numTilings, self.num_bins, stateVars)
# active_tiles = loadtiles([0], 0, self.numTilings*self.num_bins, 1024, stateVars)
# buffer = [0] # array of length numtilings
# tiles(1,512,stateVars)
# active_tiles = loadtiles([0], 0, self.numTilings, self.num_bins, stateVars)
self.F = active_tiles = tiles(self.numTilings,self.mem_size,stateVars)
# print 'tiles = ' + str(active_tiles)
# active_tiles = simple_tiles(self.numTilings, self.numTilings*self.num_bins, stateVars)
# simple_tiles(self.numTilings, self.numTilings*self.num_bins, stateVars)
# print "featureVector " + str(len(self.theta))
# print 'active tiles = ' + str(active_tiles)
# print 'numTilings = ' + str(self.numTilings)
# print 'stateVars = ' + str(stateVars)
"""
def featurize(s):
normS = normalize(s)
featvecs = Parallel(n_jobs=num_cores)(delayed(getfeatvec)(resolutions[res], normS) for res in range(len(resolutions)))
# Concatenate the feature vectors and add
# Single active baseline unit
catfeat = np.hstack(featvecs)
catfeat = np.hstack((0, catfeat))
print catfeat[0]
print len(catfeat)
print sum(np.power(np.array(resolutions), numFeatures)*numTilings)+1
# For each resolution the features are needed
for res in np.nditer(resolutions):
# Tilecoding hash table
# ctu = tiles.CollisionTable(32, 'super safe')
# ctu = tiles.CollisionTable(4096, 'unsafe')
# cts = tiles.CollisionTable(4096, 'safe')
# ctss = tiles.CollisionTable(4096, 'super safe')
resVec = np.zeros(np.power(res, numFeatures)*numTilings)
# resVecHash = np.zeros(4096)
# Get the indexes of the tiles
tilesOut = tiles.tiles(numTilings, res, normS*res)
print tilesOut
# print tilesOut
# tilesOutHash = tiles.tiles(numTilings, ctu, normS*res)
# print tilesOutHash
# resVecHash[tilesOutHash] = 1
# print resVecHash
# For each tile index flip a feature in the feature vector
# for tileIndex in range(len(tilesOut)):
# resVec[tilesOut[tileIndex] + (res**2 * tileIndex)] = 1
# x = np.append(x, resVec)
return catfeat
def __init__(self, concealed, exposed=None, initial_update=True):
if isinstance(concealed, str):
concealed = tiles.tiles(concealed)
if isinstance(concealed, Counter):
self._concealed = concealed
else:
self._concealed = Counter(concealed)
self._exposed = exposed or []
self.claimable_chow = {}
self.claimable_pung = {}
self.claimable_kong = {}
self.claimable_win = {}
self.shanten_std, self.shanten_7, self.shanten_13 = None, None, None
if initial_update:
self.update()
def getfeatvec(res, normS):
scalednormS = [x * res for x in normS]
# x is the active tiles in this featurization
# This featurization should return a single binary feature vector
# with exactly m features in x(s) active at any time
# Binary feature vector has a length of 4636426 or
# sum(np.power(np.array([5,8,12,20]),4)*25)+1
x = np.zeros(np.power(res, numFeatures)*numTilings)
tilesOut = tiles.tiles(numTilings, res, scalednormS)
for idx, val in enumerate(tilesOut):
# for each tile index put out by the tile coder
# we need to flip a bit in the feature vector
# this bit index is given by the value of the tile from the tile coder
# the index this value was at, the resolution of the tile
x[val + (res**2 * idx)] = 1
return x
def __init__(self, level, controls):
super().__init__()
self.set_background_color(33/255, 46/255, 56/255)
# set up timing system
self.timeline = Timeline()
self.last_time = 0.0
def extract_tile_id(filename, flags, tileset):
def inner(rect, flags):
x, y, w, h = rect
return x // w + y // h * tileset.columns
return inner
tile_list = tiles(self)
tiled_map = TiledMap(level, image_loader=extract_tile_id)
level = self.render.attach_new_node("level")
width = tiled_map.width
height = tiled_map.height * 3**0.5 / 2
level.set_pos(width / 2, -height / 2, 0)
def get_track(x, y):
for i, layer in enumerate(tiled_map):
tile = tile_list.get(tiled_map.get_tile_image(x, y, i))
if isinstance(tile, Track):
return tile
return None
z = {}
for layer in tiled_map:
for x, y, tile_id in layer.tiles():
if (tile_type := tile_list.get(tile_id)) is not None:
tile = level.attach_new_node("tile")
tile.set_pos(*from_hex(x, y), z.get((x, y), 0))
z[(x, y)] = z.get((x, y), 0) + tile_type.height
tile_type.instance_to(tile)
tile_type.register(tile, self.timeline, x, y, get_track)
def loadXml(self,node): self.getText(node.childNodes) if node.nodeType!=Node.ELEMENT_NODE: for n in node.childNodes: if n.nodeType==Node.ELEMENT_NODE and n.localName == 'xdl_resource_report': self.loadXml(n) else: for n in node.childNodes: if n.nodeType==Node.ELEMENT_NODE and n.localName == 'tiles': el=tiles() el.loadXml(n) self.set_tiles(el) for n in node.childNodes: if n.nodeType==Node.ELEMENT_NODE and n.localName == 'primitive_defs': el=primitive_defs() el.loadXml(n) self.set_primitive_defs(el) for n in node.childNodes: if n.nodeType==Node.ELEMENT_NODE and n.localName == 'summary': el=summary() el.loadXml(n) self.set_summary(el) if node.hasAttributes(): attrs=node.attributes attrId='a1' if attrId in attrs.keys(): self.a1=str(attrs[attrId].value) attrId='a0' if attrId in attrs.keys(): self.a0=str(attrs[attrId].value) attrId='a2' if attrId in attrs.keys(): self.a2=str(attrs[attrId].value)
def run(args):
"""main entry point"""
player_hand = tiles.tiles(args.hand)
func = {
'13': count_shanten_13_orphans,
'7': count_shanten_seven_pairs,
'std': count_shanten_std,
}.get(args.algorithm, None)
if func:
shanten = func(player_hand)
else:
shanten, func = count_shanten_naive(player_hand)
shanten_type = {
count_shanten_13_orphans: ShantenType.THIRTEEN_ORPHANS,
count_shanten_seven_pairs: ShantenType.SEVEN_PAIRS,
count_shanten_std: ShantenType.STANDARD
}[func]
print(f'{shanten} {shanten_type}')
def diamondtiles(numtilings, memctable, floats, widths=None, ints=[]):
"returns tiles in the shape of diamonds"
floats = scalefloats(floats, widths)
floats1 = diagonalfloats(floats)
floats2 = backdiagonalfloats(floats)
return tiles.tiles(numtilings, memctable, floats1 + floats2, ints)
def __init__(self, level, controls):
super().__init__()
self.set_background_color(33 / 255, 46 / 255, 56 / 255)
# set up timing system
self.timeline = Timeline()
self.timeline.speed = 0
self.last_time = 0.0
self.tile_list = tiles(self)
def extract_tile(filename, flags, tileset):
filename = Path(filename).name
tiles = self.tile_list[filename]
def inner(rect, flags):
x, y, w, h = rect
return tiles[x // w + y // h * tileset.columns]
return inner
tiled_map = TiledMap(level, image_loader=extract_tile)
self.level = self.render.attach_new_node("level")
self.tile_nodes = self.level.attach_new_node("tiles")
width = tiled_map.width
height = tiled_map.height * 3**0.5 / 2
self.level.set_pos(width / 2, -height / 2, 0)
self.z = defaultdict(int)
self.track = {}
self.clear = {}
self.trains = []
for layer in tiled_map:
for x, y, tile_type in layer.tiles():
if tile_type is not None:
if isinstance(tile_type, Train):
train_node = tile_type.train.copyTo(self.level)
train_node.set_pos(*from_hex(x, y), self.z[x, y])
self.trains.append(
TrainInstance(tile_type, train_node, x, y))
if isinstance(tile_type, Track):
self.track[x, y] = tile_type
else:
tile = self.tile_nodes.attach_new_node("tile")
tile.set_pos(*from_hex(x, y), self.z[x, y])
self.z[x, y] += tile_type.height
self.clear[x, y] = self.clear.get(
(x, y), True) and tile_type.clear
tile_type.node.instanceTo(tile)
self.track_nodes = None
self.update_track()
self.timeline.subscribe(self.update_trains)
# use antialiasing
self.render.set_antialias(AntialiasAttrib.MMultisample)
# position camera
self.camera.set_pos(0, 8, 8)
self.camera.look_at(0, 0, 0)
self.disable_mouse()
# create a light
ambient = ambient_light(colour=(.3, .3, .3, 1))
self.ambient = self.render.attach_new_node(ambient)
self.render.set_light(self.ambient)
# create another light
directional = directional_light(colour=(1, 1, 1, 1),
direction=(-1, -2, -3))
self.directional = self.render.attach_new_node(directional)
self.render.set_light(self.directional)
# load control scheme from file
self.load_controls(controls)
self.task_mgr.add(self.loop, 'loop')
# create a ui
aspect_ratio = self.get_aspect_ratio()
self.tile_tray = self.aspect2d.attach_new_node("tile_tray")
tile_tray_bg = OnscreenImage(image='data/black.png',
pos=(0, 0, -1.66),
scale=(aspect_ratio, 0, 1),
color=(0, 0, 0, .3),
parent=self.tile_tray)
self.tile_tray.setTransparency(TransparencyAttrib.MAlpha)
self.play = OnscreenImage(image='data/play.png',
pos=(-0.9 * aspect_ratio, 0, 0.85),
scale=0.08,
parent=self.aspect2d)
self.play.setTransparency(TransparencyAttrib.MAlpha)
self.stop = OnscreenImage(image='data/stop.png',
pos=(-0.9 * aspect_ratio, 0, 0.85),
scale=0.08,
parent=self.aspect2d)
self.stop.setTransparency(TransparencyAttrib.MAlpha)
self.stop.hide()
self.playing = False
track_id_to_thumb = {
1: 'straight_1-2-3-4.png',
2: 'curved_1-2-3-4.png',
3: 'straight_1-_-3-4.png',
4: 'straight_1-2-_-4.png',
5: 'straight_1-2-3-_.png',
6: 'curved_1-_-3-4.png',
7: 'curved_1-2-_-4.png',
8: 'curved_1-2-3-_.png',
}
self.thumbs = random.choices(list(track_id_to_thumb), k=3)
self.selected_thumb = None
for n, thumb in enumerate(self.thumbs):
_thumb = OnscreenImage(image='thumbs/' + track_id_to_thumb[thumb],
pos=((n + 1) * 2 / (len(self.thumbs) + 1) -
1, 0, -.82),
scale=.15,
parent=self.tile_tray)
_thumb.setTransparency(TransparencyAttrib.MAlpha)
self.preview = self.level.attach_new_node("preview")
self.preview.setTransparency(TransparencyAttrib.MAlpha)
self.preview.setColorScale(2, 2, 2, 0.65)
self.preview.hide()
self.rotating_cw = False
self.rotating_ccw = False
self.mouse_handler = MouseHandler(self.camera, self.tile_nodes)
def exptiles(numtilings, memctable, floats, ints=[]):
"returns tiles which vary in size exponentially from large to small"
floats = expfloats(floats)
return tiles.tiles(numtilings, memctable, floats, ints)
def logtiles(numtilings, memctable, floats, ints=[]):
"returns tiles which vary in size logarithmically from small to large"
floats = logfloats(floats)
return tiles.tiles(numtilings, memctable, floats, ints)
def scaletiles(numtilings, memctable, floats, widths=None, ints=[]):
"returns tiles scaled by widths"
floats = scalefloats(floats, widths)
return tiles.tiles(numtilings, memctable, floats, ints)
def terminal_update(self, reward, state, action):
""" Update the weights after a terminal step. """
x = tiles.tiles(self.iht, self.no_of_tiles, np.append(state, action)*self.normalise)
q = sum([self.weights[x[i]][i] for i in range(self.no_of_tiles)])
for i in range(self.no_of_tiles):
self.weights[x[i]][i] += self.training_rate*(reward - q)
def runit (num=10, ct=2048, numt=1):
for i in xrange(num):
for j in xrange(num):
t = tiles.tiles(numt, ct, [i*0.5, j*0.5])
def logtiles (numtilings, memctable, floats, ints=[]):
"returns tiles which vary in size logarithmically from small to large"
floats = logfloats(floats)
return tiles.tiles(numtilings, memctable, floats, ints)
def scaletiles (numtilings, memctable, floats, widths=None, ints=[]):
"returns tiles scaled by widths"
floats = scalefloats(floats, widths)
return tiles.tiles(numtilings, memctable, floats, ints)
def runit2 (num=10, ct=2048, numt=1):
for i in xrange(num):
for j in xrange(num):
t = tiles.tiles(numt, ct, [i*0.5, j*0.5, float(i+j)/2, float(i-j)/2], [i, j])
def get_features(self, pos, vel):
return np.array(tiles(self.iht, self.num_tiles, [self.num_tilings * pos / (0.5 + 1.2), self.num_tilings * vel / (0.07 + 0.07)]))
pcaT_all_cen, pcaT_info_cen, fastT_info_cen = [], [], []
print 'Processing '+ infile+'\n'
#k = m = 1
for s in range(k*m):
s += 1; print 'Complete:'+str(s)+' of '+ str(k*m) + ';'
train_table = uxvaltables[s]['train']['0']; test_table = uxvaltables[s]['test']['0']
discrete_table = discrete.discrete(train_table, bins) #discrete_table[0]: original; discrete_table['d']: discrete table
attrlst, inforgain = attrselect.attrselect(discrete_table['d'], pattr) #select attributes
# tables after feature selection
attrtable_train = attrselect.attrtable(train_table, attrlst)
attrtable_test = attrselect.attrtable(test_table, attrlst)
#test_table = projections.projections(uxvaltables[s]['test'])
pcaT_all,tmp1 = tiles.tiles(table, numdim, outfile0) #pca projection and clustering
pcaT_info, tmp2 = tiles.tiles(attrtable_train, numdim, outfile0)
#fastT_all = tiles.tilesv2(table, numdim, outfile0)
fastT_info, tmp3 = tiles.tilesv2(attrtable_train, numdim, outfile0)
pcaT_all_cen += [tmp1]; pcaT_info_cen += [tmp2]; fastT_info_cen += [tmp3]
#tablestr.tableprint(centroid_attr)
tables = reader.klasses(table)
kt = 1; mt = 2
acc1, f1, prec1, pd1 = nb.nb(xvaltables[s]['test'], xvaltables[s]['train'], tables['names'], kt, mt)
acc2, f2, prec2, pd2 = knn.knn(uxvaltables[s]['test'], uxvaltables[s]['train'], kn)
# PCA Infer Methods
acc3, f3, prec3, pd3 = newknn.knn(test_table, pcaT_all[0], kn, threshold)
acc4, f4, prec4, pd4 = newknn.knn(attrtable_test, pcaT_info[0], kn, threshold)
import reader
import tablestr
import tiles
import math
if __name__ == "__main__":
filename = 'data/nasa93dem.csv'
table = tablestr.Table()
reader.readcsv(filename, table)
tables = tiles.tiles(table)
ntable = tables['project']
print '# $_XX'.ljust(8), '$_YY'.ljust(8), 'log(-effort)'.ljust(8)
for i in range(len(ntable.data[0])):
print ntable.data[27][i].ljust(8), ntable.data[28][i].ljust(8), str(math.log(float(ntable.data[24][i]))).ljust(8)
print '# $_XX'.ljust(8), '$_YY'.ljust(8), 'log($_ZZ)'.ljust(8)
for i in range(len(tables[0].data[0])):
print tables[0].data[27][i].ljust(8), tables[0].data[28][i].ljust(8), str(math.log(float(tables[0].data[30][i]))).ljust(8)
for k, v in tables.items():
print '*'*20
print 'CLASS LABEL: ', k
tablestr.tableprint(v)
def main():
t = tiles()
while True:
t.display_hands()
input("Please input something")
t.display_answer()
if __name__ == '__main__':
loadPrcFileData("", """
window-type none
show-frame-rate-meter 0
sync-video 0
""" )
aspect = 1.5
width = 128
height = int(128 * aspect)
base = ShowBase()
tile_lists = tiles(base)
base.set_background_color(33/255, 46/255, 56/255)
# use antialiasing
base.render.setAntialias(AntialiasAttrib.MMultisample)
# move camera
lens = OrthographicLens()
# TODO: How do the default camera controls work?
base.disable_mouse()
# create a light
ambient = ambient_light((.3, .3, .3, 1))
ambient = base.render.attach_new_node(ambient)
def exptiles (numtilings, memctable, floats, ints=[]):
"returns tiles which vary in size exponentially from large to small"
floats = expfloats(floats)
return tiles.tiles(numtilings, memctable, floats, ints)
def diamondtiles(numtilings, memctable, floats, widths=None, ints=[]):
"returns tiles in the shape of diamonds"
floats = scalefloats(floats, widths)
floats1 = diagonalfloats(floats)
floats2 = backdiagonalfloats(floats)
return tiles.tiles(numtilings, memctable, floats1+floats2, ints)
def runit(num=10, ct=2048, numt=1):
for i in xrange(num):
for j in xrange(num):
t = tiles.tiles(numt, ct, [i * 0.5, j * 0.5])
def stripetiles(numtilings, memctable, floats, widths=None, ints=[]):
"returns tiles in the shape of stripes (scaled by widths), a set for each dimension in floats"
floats = scalefloats(floats, widths)
# should another int be added here for each dimension if there is more than one?
return reduce(operator.add, [tiles.tiles(numtilings, memctable, [f], ints) for f in floats])
