def showFeatureSpace(X, W, rho):
plt.figure()
filename = "feature(" + str(W) + "," + str(rho) + ")2g.png"
print("==================")
classSize = len(W)
canContinue = True
for i in range(len(X)):
if X[i][2] == 1:
plt.scatter(X[i][1], 0, c='green', s=50, marker="o")
elif X[i][2] == 2:
plt.scatter(X[i][1], 0, c='yellow', s=50, marker="s")
while canContinue:
canContinue = False
cntIncorrectClass = 0
for i in range(len(X)):
g = []
for c in range(classSize):
g.append(mathfunction.g(X[i], W[c]))
maxClass = np.nanargmax(g) + 1
rightClass = X[i][2]
print g
if maxClass != rightClass:
print str(rightClass) + "→" + str(maxClass)
W[rightClass - 1][0] += rho * X[i][0]
W[rightClass - 1][1] += rho * X[i][1]
W[maxClass - 1][0] -= rho * X[i][0]
W[maxClass - 1][1] -= rho * X[i][1]
canContinue = True
cntIncorrectClass += 1
print("IncorrectClass:" + str(cntIncorrectClass))
# 学習後識別関数の表示
showGfunction("black", 1)
showGfunction("red", 2)
intersection_x = (W[0][0] - W[1][0]) / (W[1][1] - W[0][1])
intersection_y = (W[1][1] * W[0][0] - W[0][1] * W[1][0]) / (W[1][1] -
W[0][1])
plt.plot([intersection_x, intersection_x], [0, intersection_y],
color="blue",
linewidth=2,
linestyle='--')
matplotConfig.forFeature(filename, 0)
plt.savefig(filename) #CUIでshowは使えない
def showFeatureSpace(X, W , rho):
plt.figure()
filename = "feature(" + str(W) + "," + str(rho) + ")2g.png"
print("==================")
classSize = len(W)
canContinue = True
for i in range(len(X)):
if X[i][2] == 1:
plt.scatter(X[i][1], 0, c = 'green', s = 50, marker = "o")
elif X[i][2] == 2:
plt.scatter(X[i][1], 0, c = 'yellow', s= 50, marker = "s")
while canContinue:
canContinue = False
cntIncorrectClass = 0
for i in range(len(X)):
g = []
for c in range(classSize):
g.append(mathfunction.g(X[i], W[c]))
maxClass = np.nanargmax(g) + 1
rightClass = X[i][2]
print g
if maxClass != rightClass:
print str(rightClass) + "→" + str(maxClass)
W[rightClass-1][0] += rho * X[i][0]
W[rightClass-1][1] += rho * X[i][1]
W[maxClass-1][0] -= rho * X[i][0]
W[maxClass-1][1] -= rho * X[i][1]
canContinue = True
cntIncorrectClass += 1
print("IncorrectClass:" + str(cntIncorrectClass))
# 学習後識別関数の表示
showGfunction("black",1)
showGfunction("red",2)
intersection_x = (W[0][0] - W[1][0]) / (W[1][1] - W[0][1])
intersection_y = (W[1][1]*W[0][0] - W[0][1]*W[1][0]) / (W[1][1] - W[0][1])
plt.plot([intersection_x, intersection_x],[0, intersection_y], color = "blue", linewidth = 2, linestyle = '--')
matplotConfig.forFeature(filename, 0)
plt.savefig(filename) #CUIでshowは使えない
def showWeightSpace(X, W, row):
plt.figure()
filename = "weight(" + str(W[1]) + "," + str(
W[0]) + "," + str(row) + ").png"
W_copy = list(W)
canContinue = True
plt.scatter(W[1], W[0], c='blue', marker="o")
while canContinue:
canContinue = False
for i in range(len(X)):
g_value = mathfunction.g(X[i], W)
print(str(X[i]) + ":" + str(W) + ":" + str(g_value))
if g_value < 0 and X[i][2] == 1:
print("1→2")
W[0] = W[0] + row * X[i][0]
W[1] = W[1] + row * X[i][1]
canContinue = True
plt.scatter(W[1], W[0], c='red', marker="o")
plt.plot([W_copy[1], W[1]], [W_copy[0], W[0]], color="red")
W_copy = list(W)
elif g_value > 0 and X[i][2] == 2:
print("2→1")
W[0] = W[0] - row * X[i][0]
W[1] = W[1] - row * X[i][1]
canContinue = True
plt.scatter(W[1], W[0], c='red', marker="o")
plt.plot([W_copy[1], W[1]], [W_copy[0], W[0]], color="red")
W_copy = list(W)
# 解領域の表示
w1 = x = np.linspace(-3, 13)
for i in range(len(X)):
if X[i][2] == 1:
color = '#ffff00'
elif X[i][2] == 2:
color = '#00ff00'
w0 = -X[i][1] * w1
plt.plot(w1, w0, color, linewidth=1.0, linestyle="-")
matplotConfig.forWeight(filename)
plt.savefig(filename) #CUIでshowは使えない
def widrowhoff():
classSize = len(B)
epoch = 100
for ep in range(epoch):
print("---"+ str(ep+1) + "th---")
total_epsilon = 0
for p in range(len(X)):
g_v = []
e_v = []
p_total_epsilon = 0
for i in range(classSize):
g = mathfunction.g(X[p], W[i])
e = g - B[X[p][2]-1][i]
W[i][0] -= rho * X[p][0] * e
W[i][1] -= rho * X[p][1] * e
g_v.append(g)
e_v.append(e)
p_total_epsilon = math.fabs(e_v[i])
g_str = ""
B_str = ""
e_str = ""
for i in range(classSize):
g_str += str(g_v[i]) + ","
B_str += str(B[X[p][2]-1][i]) + ","
e_str += str(e_v[i]) + ","
print("Xp:" + str(p)
+ " g:[" + g_str
+ "] B:[" + B_str
+ "] e:[" + e_str + "]"
+ "=" + str(p_total_epsilon))
for i in range(classSize):
total_epsilon += p_total_epsilon
print("total_e:" + str(total_epsilon))
def showWeightSpace(X, W, row):
plt.figure()
filename = "weight(" + str(W[1]) + "," + str(W[0]) + "," + str(row) + ").png"
W_copy = list(W)
canContinue = True
plt.scatter(W[1], W[0], c = 'blue', marker = "o")
while canContinue:
canContinue = False
for i in range(len(X)):
g_value = mathfunction.g(X[i], W)
print(str(X[i]) + ":" + str(W) + ":" + str(g_value))
if g_value < 0 and X[i][2] == 1:
print("1→2")
W[0] = W[0] + row * X[i][0]
W[1] = W[1] + row * X[i][1]
canContinue = True
plt.scatter(W[1], W[0], c = 'red', marker = "o")
plt.plot([W_copy[1], W[1]], [W_copy[0], W[0]], color="red")
W_copy = list(W)
elif g_value > 0 and X[i][2] == 2:
print("2→1")
W[0] = W[0] - row * X[i][0]
W[1] = W[1] - row * X[i][1]
canContinue = True
plt.scatter(W[1], W[0], c = 'red', marker = "o")
plt.plot([W_copy[1], W[1]], [W_copy[0], W[0]], color="red")
W_copy = list(W)
# 解領域の表示
w1 = x = np.linspace(-3, 13)
for i in range(len(X)):
if X[i][2] == 1:
color = '#ffff00'
elif X[i][2] == 2:
color = '#00ff00'
w0 = -X[i][1] * w1
plt.plot(w1, w0, color, linewidth=1.0, linestyle="-")
matplotConfig.forWeight(filename)
plt.savefig(filename) #CUIでshowは使えない
def widrowhoff():
classSize = len(B)
epoch = 100
for ep in range(epoch):
print("---" + str(ep + 1) + "th---")
total_epsilon = 0
for p in range(len(X)):
g_v = []
e_v = []
p_total_epsilon = 0
for i in range(classSize):
g = mathfunction.g(X[p], W[i])
e = g - B[X[p][2] - 1][i]
W[i][0] -= rho * X[p][0] * e
W[i][1] -= rho * X[p][1] * e
g_v.append(g)
e_v.append(e)
p_total_epsilon = math.fabs(e_v[i])
g_str = ""
B_str = ""
e_str = ""
for i in range(classSize):
g_str += str(g_v[i]) + ","
B_str += str(B[X[p][2] - 1][i]) + ","
e_str += str(e_v[i]) + ","
print("Xp:" + str(p) + " g:[" + g_str + "] B:[" + B_str + "] e:[" +
e_str + "]" + "=" + str(p_total_epsilon))
for i in range(classSize):
total_epsilon += p_total_epsilon
print("total_e:" + str(total_epsilon))
def showFeatureSpace(X, W, row):
plt.figure()
filename = "feature(" + str(W[1]) + "," + str(
W[0]) + "," + str(row) + ").png"
canContinue = True
showGfunction("blue")
for i in range(len(X)):
if X[i][2] == 1:
plt.scatter(X[i][1], 0, c='green', s=50, marker="o")
elif X[i][2] == 2:
plt.scatter(X[i][1], 0, c='yellow', s=50, marker="s")
while canContinue:
canContinue = False
for i in range(len(X)):
g_value = mathfunction.g(X[i], W)
print(str(X[i]) + ":" + str(W) + ":" + str(g_value))
if g_value < 0 and X[i][2] == 1:
print("1→2")
W[0] = W[0] + row * X[i][0]
W[1] = W[1] + row * X[i][1]
showGfunction("red")
canContinue = True
elif g_value > 0 and X[i][2] == 2:
print("2→1")
W[0] = W[0] - row * X[i][0]
W[1] = W[1] - row * X[i][1]
showGfunction("red")
canContinue = True
# 学習後識別関数の表示
showGfunction("black")
matplotConfig.forFeature(filename, -2)
plt.savefig(filename) #CUIでshowは使えない
def showFeatureSpace(X, W , row):
plt.figure()
filename = "feature(" + str(W[1]) + "," + str(W[0]) + "," + str(row) + ").png"
canContinue = True
showGfunction("blue")
for i in range(len(X)):
if X[i][2] == 1:
plt.scatter(X[i][1], 0, c = 'green', s = 50, marker = "o")
elif X[i][2] == 2:
plt.scatter(X[i][1], 0, c = 'yellow', s= 50, marker = "s")
while canContinue:
canContinue = False
for i in range(len(X)):
g_value = mathfunction.g(X[i], W)
print(str(X[i]) + ":" + str(W) + ":" + str(g_value))
if g_value < 0 and X[i][2] == 1:
print("1→2")
W[0] = W[0] + row * X[i][0]
W[1] = W[1] + row * X[i][1]
showGfunction("red")
canContinue = True
elif g_value > 0 and X[i][2] == 2:
print("2→1")
W[0] = W[0] - row * X[i][0]
W[1] = W[1] - row * X[i][1]
showGfunction("red")
canContinue = True
# 学習後識別関数の表示
showGfunction("black")
matplotConfig.forFeature(filename, -2)
plt.savefig(filename) #CUIでshowは使えない
def widrowhoff():
classSize = len(B)
epoch = 10
filename = "linearlyseparable.png"
plt.figure()
plt.text(0, -20, "epoch=" + str(epoch), ha = 'center', va = 'bottom', size = 'xx-large')
for i in range(len(X)):
if X[i][2] == 1:
plt.scatter(X[i][1], 0, c = 'black', s = 50, marker = "o")
elif X[i][2] == 2:
plt.scatter(X[i][1], 0, c = 'black', s= 50, marker = "x")
showG1minusG2function("blue", "start")
for i in range(epoch):
print("---"+ str(i+1) + "th---")
total_epsilon = 0
for p in range(len(X)):
g = []
for c in range(classSize):
g.append(mathfunction.g(X[p], W[c]))
if X[p][2] == 1:
epsilon = []
for w_c in range(classSize):
e = g[w_c] - B[0][w_c]
W[w_c][0] -= rho * e * X[p][0]
W[w_c][1] -= rho * e * X[p][1]
epsilon.append(e)
print("Xp:" + str(p) + " g:[" + str(g[0]) + "," + str(g[1])
+ "] B:[" + str(B[0][0]) + "," + str(B[0][1]) + "] e:["
+ str(epsilon[0]) + "," + str(epsilon[1]) + "]")
# w_c : 各クラスの識別関数gに用いる重み
for w_c in range(classSize):
total_epsilon +=math.fabs(epsilon[w_c])
elif X[p][2] == 2:
epsilon = []
for w_c in range(classSize):
e = g[w_c] - B[1][w_c]
W[w_c][0] -= rho * e * X[p][0]
W[w_c][1] -= rho * e * X[p][1]
epsilon.append(e)
print("Xp:" + str(p) + " g:[" + str(g[0]) + "," + str(g[1])
+ "] B:[" + str(B[1][0]) + "," + str(B[1][1]) + "] e:["
+ str(epsilon[0]) + "," + str(epsilon[1]) + "]")
for w_c in range(classSize):
total_epsilon +=math.fabs(epsilon[w_c])
print("total_e:" + str(total_epsilon))
showG1minusG2function("red", "")
# 学習後識別関数の表示
showG1minusG2function("green", "finish")
matplotConfig.forFeature(filename)
plt.savefig(filename) #CUIでshowは使えない
def widrowhoff():
classSize = len(B)
epoch = 10
filename = "linearlyseparable.png"
plt.figure()
plt.text(0,
-20,
"epoch=" + str(epoch),
ha='center',
va='bottom',
size='xx-large')
for i in range(len(X)):
if X[i][2] == 1:
plt.scatter(X[i][1], 0, c='black', s=50, marker="o")
elif X[i][2] == 2:
plt.scatter(X[i][1], 0, c='black', s=50, marker="x")
showG1minusG2function("blue", "start")
for i in range(epoch):
print("---" + str(i + 1) + "th---")
total_epsilon = 0
for p in range(len(X)):
g = []
for c in range(classSize):
g.append(mathfunction.g(X[p], W[c]))
if X[p][2] == 1:
epsilon = []
for w_c in range(classSize):
e = g[w_c] - B[0][w_c]
W[w_c][0] -= rho * e * X[p][0]
W[w_c][1] -= rho * e * X[p][1]
epsilon.append(e)
print("Xp:" + str(p) + " g:[" + str(g[0]) + "," + str(g[1]) +
"] B:[" + str(B[0][0]) + "," + str(B[0][1]) + "] e:[" +
str(epsilon[0]) + "," + str(epsilon[1]) + "]")
# w_c : 各クラスの識別関数gに用いる重み
for w_c in range(classSize):
total_epsilon += math.fabs(epsilon[w_c])
elif X[p][2] == 2:
epsilon = []
for w_c in range(classSize):
e = g[w_c] - B[1][w_c]
W[w_c][0] -= rho * e * X[p][0]
W[w_c][1] -= rho * e * X[p][1]
epsilon.append(e)
print("Xp:" + str(p) + " g:[" + str(g[0]) + "," + str(g[1]) +
"] B:[" + str(B[1][0]) + "," + str(B[1][1]) + "] e:[" +
str(epsilon[0]) + "," + str(epsilon[1]) + "]")
for w_c in range(classSize):
total_epsilon += math.fabs(epsilon[w_c])
print("total_e:" + str(total_epsilon))
showG1minusG2function("red", "")
# 学習後識別関数の表示
showG1minusG2function("green", "finish")
matplotConfig.forFeature(filename)
plt.savefig(filename) #CUIでshowは使えない