def plotStats(fileName):
# read in a playlist
plist = plistlib.readPlist(fileName)
# get tracks from playlist
tracks = plist['Tracks']
# create lists of song ratings and track durations
ratings = []
durations = []
# iterate through the tracks
for trackId, track in tracks.items():
try:
ratings.append(track['Album Rating'])
durations.append(track['Total Time'])
except:
#ignore
pass
# ensure valid data was collected
if ratings == [] or durations == []:
print("No valid Album Rating/Total Time data in %s." % fileName)
return
# scatter plot
x = np.array(durations, np.int32)
# convert to minutes
x = x/60000.0
y = np.array(ratings, np.int32)
pyplot.subplot(2, 1, 1)
pyplot(x, y, 'o')
pyplot.axis([0, 1.05*np.max(x), -1, 110])
pyplot.xlabel('Track duration')
pyplot.ylabel('Track rating')
# plot histogram
pyplot.subplot(2, 1, 2)
pyplot.hist(x, bins=20)
pyplot.xlabel('Track duration')
pyplot.ylabel('Count')
# show plot
pyplot.show()
def main():
points=get_point_data('./scene0000_00_vh_clean_2.labels.ply')
points=points[:]
print(points)
vols=oint_cloud_to_volume(points,4096,radius=1.0)
pyplot(vol)
for point in pointslist[i]:
p = Point(point[0], point[1])
if p.within(boundary) or checkPointOnBoundary(p):
w[i].point(p.x, p.y)
w[i].field('%d_FLD' % c)
c += 1
"""
if len(w[i].shapes())>=2:
w[i].save(writePath)
"""
print("Number of shapes = %d \n\n" % len(w))
n = 0
for shape in w:
print("shape %d contains %d points" % (n + 1, len(w[i].shapes())))
n += 1
for wr in w:
for s in wr.shapes():
x = []
y = []
for p in s.points:
print(p)
x.append(p.x)
y.append(p.y)
pyplot(x, y)
pyplot.xlim(-5, 5)
pyplot.ylim(-5, 5)
pyplot.show()
p = Point(point[0],point[1])
if p.within(boundary) or checkPointOnBoundary(p):
w[i].point(p.x,p.y)
w[i].field('%d_FLD' % c)
c +=1
"""
if len(w[i].shapes())>=2:
w[i].save(writePath)
"""
print("Number of shapes = %d \n\n" % len(w))
n = 0
for shape in w:
print("shape %d contains %d points" %(n+1, len(w[i].shapes())))
n += 1
for wr in w:
for s in wr.shapes():
x=[];y=[]
for p in s.points:
print(p)
x.append(p.x)
y.append(p.y)
pyplot(x,y)
pyplot.xlim(-5, 5)
pyplot.ylim(-5, 5)
pyplot.show()
# 37. 頻度上位10語
# 出現頻度が高い10語とその出現頻度をグラフ（例えば棒グラフなど）で表示せよ．
import matplotlib as plt
frequency = __import__('36').frequency
frequency = sorted(frequency.items(), key=lambda x:x[1], reverse=True)
frequency[:10]
plt.pyplot()
K = kahan_matrix(n, theta)
print('Computed svd rank of matrix B:', compute_rank_svd(B, thres))
print('Computed qr rank of matrix B:', compute_rank_qr(B, thres))
print('Computed lu rank of matrix B:', compute_rank_lu(B, thres))
print('Computed python rank matric B:', np.linalg.matrix_rank(B))
print('Computed svd rank of matrix A:', compute_rank_svd(A, thres))
print('Computed qr rank of matrix A:', compute_rank_qr(A, thres))
print('Computed lu rank of matrix A:', compute_rank_lu(A, thres))
print('Computed python rank matric A:', np.linalg.matrix_rank(A))
print('Computed svd rank of matrix C:', compute_rank_svd(C, thres))
print('Computed qr rank of matrix C:', compute_rank_qr(C, thres))
print('Computed lu rank of matrix C:', compute_rank_lu(C, thres))
print('Computed python rank matric C:', np.linalg.matrix_rank(C))
print('Computed svd rank of matrix D:', compute_rank_svd(D, thres))
print('Computed qr rank of matrix D:', compute_rank_qr(D, thres))
print('Computed lu rank of matrix D:', compute_rank_lu(D, thres))
print('Computed python rank matric D:', np.linalg.matrix_rank(D))
print('Computed svd rank of matrix K:', compute_rank_svd(K, thres))
print('Computed qr rank of matrix K:', compute_rank_qr(K, thres))
print('Computed lu rank of matrix K:', compute_rank_lu(K, thres))
print('Computed python rank matric K:', np.linalg.matrix_rank(K))
#%% kladd
matplotlib.pyplot(pyplot.imshow(K))
d = False
j = 0
#The Q-Network
while j < 99:
j+=1
#Choose an action by greedily (with e chance of random action) from the Q-network
a,allQ = sess.run([predict,Qout],feed_dict={inputs1:np.identity(16)[s:s+1]})
if np.random.rand(1) < e:
a[0] = env.action_space.sample()
#Get new state and reward from environment
s1,r,d,_ = env.step(a[0])
#Obtain the Q' values by feeding the new state through our network
Q1 = sess.run(Qout,feed_dict={inputs1:np.identity(16)[s1:s1+1]})
#Obtain maxQ' and set our target value for chosen action.
maxQ1 = np.max(Q1)
targetQ = allQ
targetQ[0,a[0]] = r + y*maxQ1
#Train our network using target and predicted Q values
_,W1 = sess.run([updateModel,W],feed_dict={inputs1:np.identity(16)[s:s+1],nextQ:targetQ})
rAll += r
s = s1
if d == True:
#Reduce chance of random action as we train the model.
e = 1./((i/50) + 10)
break
jList.append(j)
rList.append(rAll)
print("Percent of succesful episodes: " + str(sum(rList)/num_episodes) + "%")
plt.pyplot(rList)
# Uses alpha from mask
student_img.paste(earth_small, (1162, 966), mask=earth_small)
# Display
fig3, axes3 = plt.subplots(1, 2)
axes3[0].imshow(student_img, interpolation='none')
axes3[1].imshow(student_img, interpolation='none')
axes3[1].set_xlim(500, 1500)
axes3[1].set_ylim(1130, 850)
fig3.savefig('earth_eye')
print(earth_img.size)
print(earth_small.size)
print(earth_img.size[1])
#13.
"""
matplotlib.pyplot (plt) The matplotlib library helps to plot images on a coordinate plane.
numpy (np) Creates an array set for the images.
PIL manipulates the images by cropping, filtering, etc
"""
#15.
'''
a.
Line 19 calls the function subplots from the matplotlib library. The function is being
called with 2 argument(s): 1,2. The function returns 2 object(s), which is/are being assigned to ax.
b.
Line 20 calls __imshow()_ on ___ax[0]____