def cvtLCH_to_HTML(LCH_1channel):
lch_img = np.ones((2, 2, 3), dtype=np.float32) * LCH_1channel
BGR_img = color_changer.cvtLCH2BGR(lch_img)
RGB_img = cv2.cvtColor(BGR_img, cv2.COLOR_BGR2RGB)
RGB_1channel = RGB_img[0, 0]
return cvtRGB_to_HTML(RGB_1channel)
def main(root_dir, exp_name):
"""関連フォルダ・ファイルの存在確認"""
if not os.path.exists(root_dir):
print "root_dir", root_dir, "is not exist"
exit()
exp_dir = os.path.join(root_dir, exp_name)
if not os.path.exists(exp_dir):
print "exp_dir", exp_dir, "is not exist"
exit()
with open(os.path.join(exp_dir, "instance.pkl")) as fi:
lda = pickle.load(fi)
dim = 2
K = lda.K
#values=lda.phi()*(lda.theta().sum(axis=0)[np.newaxis].T)
values = lda.phi()
#values=lda.theta()
"""要素間距離の算出"""
delta = np.zeros((values.shape[0], values.shape[0], values.shape[1]),
dtype=values.dtype) #x値とy値の差の行列を格納
for i in range(values.shape[1]):
delta[:, :, i] = values[:, i, None] - values[:, i] #Noneは縦ベクトルにしているだけ
# distance between points
distance = np.sqrt((delta**2).sum(axis=-1))
pca = decomposition.PCA(dim)
pca.fit(values)
phi_pca = pca.transform(values)
fig = plt.figure()
if dim == 1:
ax = fig.add_subplot(111)
ax.scatter(phi_pca, phi_pca, s=1000, c=COLORLIST[:K])
#for k in range(K):
# ax.annotate(unicode(k+1),phi_pca[k])
"""主成分分析の結果をもとに，トピックごとの色リストを定義"""
l = 100
c = 1.
reg_phi_pca = (phi_pca - phi_pca.min()) / (
phi_pca.max() - phi_pca.min()) #0~1に正規化
#reg_phi_pca=reg_phi_pca*2-1#-1~1に変換
h_values = reg_phi_pca * np.pi
use_color_list = []
for k in range(K):
lch = np.array((l, c, h_values[k]), dtype=np.float32)
lch_img = np.ones((2, 2, 3), dtype=np.float32) * lch
BGR_img = color_changer.cvtLCH2BGR(lch_img)
RGB_img = cv2.cvtColor(BGR_img, cv2.COLOR_BGR2RGB)
RGB_1channel = RGB_img[0, 0]
use_color_list.append(cvtRGB_to_HTML(RGB_1channel))
fig2 = plt.figure()
ax2 = fig2.add_subplot(1, 1, 1)
labels = [unicode(x + 1) for x in range(lda.K)]
sample = [0.1 for i in range(lda.K)]
plt.rcParams['font.size'] = 20.0
ax2.pie(sample,
colors=use_color_list,
labels=labels,
startangle=90,
radius=0.2,
center=(0.5, 0.5),
frame=True,
counterclock=False)
#ax.set_aspect((ax.get_xlim()[1] - ax.get_xlim()[0]) / (ax.get_ylim()[1] - ax.get_ylim()[0]))
plt.axis("off")
plt.axis('equal')
elif dim == 2:
ax = fig.add_subplot(111)
ax.scatter(phi_pca[:, 0], phi_pca[:, 1], s=1000, c=COLORLIST[:K])
for k in range(K):
ax.annotate(unicode(k + 1), phi_pca[k])
center, size, angle = cv2.fitEllipse(phi_pca.astype(np.float32))
ell = Ellipse(xy=center, width=size[0], height=size[1], angle=angle)
ell.set_facecolor('none')
ax.add_artist(ell) # fitted curve
#xx=np.random.rand(10,2)
#xx[5,0]=10
#plt.scatter(xx[:,0], xx[:,1]) # raw data with randomness
#center,size,angle=cv2.fitEllipse(xx.astype(np.float32))
#ell = Ellipse(xy=center, width=size[0], height=size[1], angle=angle)
#ell.set_facecolor('none')
#ax.add_artist(ell) # fitted curve
elif dim == 3:
ax = fig.add_subplot(111, projection="3d")
ax.scatter(phi_pca[:, 0],
phi_pca[:, 1],
phi_pca[:, 2],
s=1000,
c=COLORLIST[:K])
plt.show()