def setPlayerLayout(self, ind, playerName, topPanelXY, topPanelWH):
baseWH = [600, 600]
remainingSubWidth = (glb.wh[0] - baseWH[0] * 2) // 4
if ind: pXY = [remainingSubWidth * 3 + baseWH[0], 10]
else: pXY = [remainingSubWidth, 10]
quadXY = [pXY[0], topPanelXY[1]]
quadWH = [reduceTo(baseWH[0], 50), topPanelWH[1]]
mdl.label(quadXY,
quadWH,
playerName,
51,
batch=self.batch,
group=gHeaderText,
xyPercInside=[10, 30])
mdl.quad(quadXY,
quadWH, [0, 159, 217, 150],
self.batch,
group=gHeaderQuad,
blend=True)
lblXY = [quadXY[0] + quadWH[0] + 20, quadXY[1]]
self.turnLbl[ind] = mdl.label(lblXY,
quadWH,
'Turn !',
51,
batch=self.batch,
group=gHeaderText,
xyPercInside=[10, 30])
self.lblSize = self.turnLbl[ind].font_size
self.player[ind] = Player.Player(pXY,
baseWH,
batch=self.batch,
group=gPlayer)
def to_libfm(examples, libfm_filename):
libfm_file = open(libfm_filename, 'w')
for example in examples:
x_i = model.represent(example)[:-1]
y_i = model.label(example)
user_id = example['user']
user_id_feature = "%d:1" % (len(x_i)+user_id)
libfm_file.write("%d %s %s\n" % (y_i, sp_vector(x_i), user_id_feature))
libfm_file.close()
def to_libfm(examples, libfm_filename):
libfm_file = open(libfm_filename, 'w')
for example in examples:
x_i = model.represent(example)[:-1]
y_i = model.label(example)
user_id = example['user']
user_id_feature = "%d:1" % (len(x_i) + user_id)
libfm_file.write("%d %s %s\n" % (y_i, sp_vector(x_i), user_id_feature))
libfm_file.close()
def setLabels( self, optionList ) :
self.optionList = optionList
if self.optionLabels :
for lbl in self.optionLabels : lbl.delete()
self.optionLabels = [ ]
wh = self.subWH ; wh[1] = reduceTo( wh[1], 70 )
for i in range( len( optionList ) ) :
if len( optionList[i] ) :
xy = self.indexToXY( [i,0] )
self.optionLabels.append(
mdl.label( xy, wh, optionList[i][0], size = 36, batch = self.batch, group = self.group + gText, xyPercInside = [2,45] )
)
def victoryStuffs(self):
self._status = VICTORY
self.turnLbl[self.ind].text = 'Victory !'
self.player[self.ind].makeShipsVisible()
self.player[not self.ind].makeShipsVisible()
pXY = self.player[self.ind].xy
pWH = self.player[self.ind].wh
wh = [reduceTo(pWH[0], 50), self.topPanelXY[1] - pXY[1] - pWH[1] - 10]
xy = [(glb.wh[0] - wh[0]) // 2, pXY[1] + pWH[1] + 5]
self.mainMenuButton = GameModel.GameModel(xy,
wh, [1, 1],
self.batch,
gPlayer,
mouseOverAud=True)
mXY = self.mainMenuButton.xy
mWH = self.mainMenuButton.wh
mdl.quad(mXY,
mWH, [0, 159, 217, 180],
self.batch,
gFullQuad,
blend=True)
mdl.quad([0, 0],
glb.wh, [0, 0, 0, 150],
self.batch,
gFullQuad,
blend=True)
wh[1] = reduceTo(wh[1], 85)
mdl.label(xy,
wh,
'Main Menu',
size=49,
batch=self.batch,
group=gHeaderText,
xyPercInside=[5, 20])
def get_test_examples():
global test_X
global test_y
if not test_X:
print "Loading test examples"
_log_time()
test_examples = music.load_examples("data/test_40k_10k.pkl")
_print_time_diff()
print "Obtaining X and y values"
_log_time()
test_X = [model.represent(example) for example in test_examples]
_print_time_diff()
_log_time()
test_y = [model.label(example) for example in test_examples]
_print_time_diff()
return test_X, test_y
_print_time_diff()
_draw_separator()
print "Start loading examples"
_log_time()
examples = music.load_examples("data/train.pkl")
_print_time_diff()
_draw_separator()
print "Obtaining all x and y values"
_log_time()
all_X = [model.represent(example) for example in examples]
_print_time_diff()
_log_time()
all_y = [model.label(example) for example in examples]
_print_time_diff()
_draw_separator()
def print_consolidated_scores(scores):
_draw_separator(".", 5)
print "%0.6f (+/- %0.6f)" % (scores.mean(), scores.std() / 2)
_draw_separator("`", 5)
def _get_mean_score(scores):
return "%0.6f" % (scores.mean())
test_X = None
def contains_named_entities(sentence):
pred = model.label(sentence)
if 'DOS' in pred or 'UNIT' in pred:
return True
def setHeader( self, text, quadColor ) :
self.headerXY = xy = [0, reduceTo( self.sidePanelWH[1], 80) ]
wh = [ self.sidePanelWH[0], reduceTo( self.sidePanelWH[1], 12) ]
mdl.quad( xy, wh, quadColor, self.batch, self.group + gQuad, blend = True )
wh[1] = reduceTo( wh[1], 90 )
self.headerLbl = mdl.label( xy, wh, text, size = 54, batch = self.batch, group = self.group + gText , xyPercInside = [4,25] )
return scores
if __name__ == "__main__":
import music
train_examples = music.load_examples('data/train.pkl')
# poly = PolynomialNetworkRegressor(degreex=3, n_components=2, tol=1e-3, warm_start=True, random_state=0)
fm = pylibfm.FM(num_iter=10,
verbose=True,
task="regression",
initial_learning_rate=0.001,
learning_rate_schedule="optimal")
v = DictVectorizer()
X = np.asarray([model.represent(example) for example in train_examples])
y = np.asarray([model.label(example) for example in train_examples])
# fm.fit(sparse.csr_matrix(X), y)
# svr_rbf.fit(X, y)
# pca = PCA(n_components=100)
# pca.fit(X)
# X_fit = pca.transform(X)
# print "pca done"
# xs = [x[0] for x in X_fit]
# ys = [x[1] for x in X_fit]
# plt.scatter(xs, ys)
# plt.show()
# print v.fit_transform(X)
# print X_fitM
y_np = np.asarray(y)
plt.hist(y_np, bins=np.arange(y_np.min(), y_np.max() + 1))
# plt.title("Frequency of ratings")
