def createKnnLearner(naFeatures, lKnn=30, leafsize=10, method='mean'):
'''
@summary: Creates a quick KNN learner
@param naFeatures: Numpy array of features,
@param fMin: Data frame containing the price information for all of the stocks.
@param fMax: List of feature functions, most likely coming from features.py
@param bAbsolute: If true, min value will be scaled to fMin, max to fMax, if false,
+-1 standard deviations will be scaled to fit between fMin and fMax, i.e. ~69% of the values
@param bIgnoreLast: If true, last column is ignored (assumed to be classification)
@return: None, data is modified in place
'''
cLearner = kdt.kdtknn(k=lKnn, method=method, leafsize=leafsize)
cLearner.addEvidence(naFeatures)
return cLearner
def createKnnLearner( naFeatures, lKnn=30, leafsize=10, method='mean' ):
'''
@summary: Creates a quick KNN learner
@param naFeatures: Numpy array of features,
@param fMin: Data frame containing the price information for all of the stocks.
@param fMax: List of feature functions, most likely coming from features.py
@param bAbsolute: If true, min value will be scaled to fMin, max to fMax, if false,
+-1 standard deviations will be scaled to fit between fMin and fMax, i.e. ~69% of the values
@param bIgnoreLast: If true, last column is ignored (assumed to be classification)
@return: None, data is modified in place
'''
cLearner = kdt.kdtknn( k=lKnn, method=method, leafsize=leafsize)
cLearner.addEvidence( naFeatures )
return cLearner
def main():
#
# read in and slice up the data
#
#data = np.loadtxt('data-classification-prob.csv',delimiter=',',skiprows=1)
data = np.loadtxt('data-ripple-prob.csv', delimiter=',', skiprows=1)
X1 = data[:, 0]
X2 = data[:, 1]
Y = data[:, 2]
colors = findcolors(Y)
#
# scatter plot X1 vs X2 and colors are Y
#
plt.clf()
fig = plt.figure()
fig1 = fig.add_subplot(221)
plt.scatter(X1, X2, c=colors, edgecolors='none')
plt.xlabel('X1')
plt.ylabel('X2')
plt.xlim(-1, 1) # set x scale
plt.ylim(-1, 1) # set y scale
plt.title('Training Data 2D View', fontsize=12)
# plot the 3d view
ax = fig.add_subplot(222, projection='3d')
ax.scatter(X1, X2, Y, c=colors, edgecolors='none')
#ax.scatter(X1,X2,Y,c=colors)
ax.set_xlabel('X1')
ax.set_ylabel('X2')
ax.set_zlabel('Y')
ax.set_xlim3d(-1, 1)
ax.set_ylim3d(-1, 1)
ax.set_zlim3d(-1, 1)
plt.title('Training Data 3D View', fontsize=12)
##########
# OK, now create and train a learner
#
learner = kdt.kdtknn(k=30, method='mean')
numpoints = X1.shape[0]
dataX = np.zeros([numpoints, 2])
dataX[:, 0] = X1
dataX[:, 1] = X2
trainsize = floor(dataX.shape[0] * .6)
learner.addEvidence(dataX[0:trainsize], dataY=Y[0:trainsize])
steps = 50.0
stepsize = 2.0 / steps
Xtest = np.zeros([steps * steps, 2])
count = 0
for i in np.arange(-1, 1, stepsize):
for j in np.arange(-1, 1, stepsize):
Xtest[count, 0] = i + stepsize / 2
Xtest[count, 1] = j + stepsize / 2
count = count + 1
Ytest = learner.query(Xtest) # to check every point
#
# Choose colors
#
colors = findcolors(Ytest)
#
# scatter plot X1 vs X2 and colors are Y
#
fig1 = fig.add_subplot(223)
plt.scatter(Xtest[:, 0], Xtest[:, 1], c=colors, edgecolors='none')
plt.xlabel('X1')
plt.ylabel('X2')
plt.xlim(-1, 1) # set x scale
plt.ylim(-1, 1) # set y scale
plt.title('Learned Model 2D', fontsize=12)
# plot the 3d view
ax = fig.add_subplot(224, projection='3d')
ax.scatter(Xtest[:, 0], Xtest[:, 1], Ytest, c=colors, edgecolors='none')
#X1 = Xtest[:,0]
#X2 = Xtest[:,1]
#X1 = np.reshape(X1,(steps,steps))
#X2 = np.reshape(X2,(steps,steps))
#Ytest = np.reshape(Ytest,(steps,steps))
ax.set_xlabel('X1')
ax.set_ylabel('X2')
ax.set_zlabel('Y')
ax.set_xlim3d(-1, 1)
ax.set_ylim3d(-1, 1)
ax.set_zlim3d(-1, 1)
plt.title('Learned Model 3D', fontsize=12)
savefig("scatterdata3D.png", format='png')
plt.close()
#
# Compare to ground truth
#
print 'trainsize ' + str(trainsize)
Ytruth = Y[-trainsize:]
print 'Ytruth.shape ' + str(Ytruth.shape)
Xtest = dataX[-trainsize:, :]
print 'Xtest.shape ' + str(Xtest.shape)
Ytest = learner.query(Xtest) # to check every point
print 'Ytest.shape ' + str(Ytest.shape)
plt.clf()
plt.scatter(Ytruth, Ytest, edgecolors='none')
plt.xlim(-1.2, 1.2) # set x scale
plt.ylim(-1.2, 1.2) # set y scale
plt.xlabel('Ground Truth')
plt.ylabel('Estimated')
savefig("scatterdata.png", format='png')
print corrcoef(Ytruth, Ytest)
def main():
#
# read in and slice up the data
#
# data = np.loadtxt('data-classification-prob.csv',delimiter=',',skiprows=1)
data = np.loadtxt("data-ripple-prob.csv", delimiter=",", skiprows=1)
X1 = data[:, 0]
X2 = data[:, 1]
Y = data[:, 2]
colors = findcolors(Y)
#
# scatter plot X1 vs X2 and colors are Y
#
plt.clf()
fig = plt.figure()
fig1 = fig.add_subplot(221)
plt.scatter(X1, X2, c=colors, edgecolors="none")
plt.xlabel("X1")
plt.ylabel("X2")
plt.xlim(-1, 1) # set x scale
plt.ylim(-1, 1) # set y scale
plt.title("Training Data 2D View", fontsize=12)
# plot the 3d view
ax = fig.add_subplot(222, projection="3d")
ax.scatter(X1, X2, Y, c=colors, edgecolors="none")
# ax.scatter(X1,X2,Y,c=colors)
ax.set_xlabel("X1")
ax.set_ylabel("X2")
ax.set_zlabel("Y")
ax.set_xlim3d(-1, 1)
ax.set_ylim3d(-1, 1)
ax.set_zlim3d(-1, 1)
plt.title("Training Data 3D View", fontsize=12)
##########
# OK, now create and train a learner
#
learner = kdt.kdtknn(k=30, method="mean")
numpoints = X1.shape[0]
dataX = np.zeros([numpoints, 2])
dataX[:, 0] = X1
dataX[:, 1] = X2
trainsize = floor(dataX.shape[0] * 0.6)
learner.addEvidence(dataX[0:trainsize], dataY=Y[0:trainsize])
steps = 50.0
stepsize = 2.0 / steps
Xtest = np.zeros([steps * steps, 2])
count = 0
for i in np.arange(-1, 1, stepsize):
for j in np.arange(-1, 1, stepsize):
Xtest[count, 0] = i + stepsize / 2
Xtest[count, 1] = j + stepsize / 2
count = count + 1
Ytest = learner.query(Xtest) # to check every point
#
# Choose colors
#
colors = findcolors(Ytest)
#
# scatter plot X1 vs X2 and colors are Y
#
fig1 = fig.add_subplot(223)
plt.scatter(Xtest[:, 0], Xtest[:, 1], c=colors, edgecolors="none")
plt.xlabel("X1")
plt.ylabel("X2")
plt.xlim(-1, 1) # set x scale
plt.ylim(-1, 1) # set y scale
plt.title("Learned Model 2D", fontsize=12)
# plot the 3d view
ax = fig.add_subplot(224, projection="3d")
ax.scatter(Xtest[:, 0], Xtest[:, 1], Ytest, c=colors, edgecolors="none")
# X1 = Xtest[:,0]
# X2 = Xtest[:,1]
# X1 = np.reshape(X1,(steps,steps))
# X2 = np.reshape(X2,(steps,steps))
# Ytest = np.reshape(Ytest,(steps,steps))
ax.set_xlabel("X1")
ax.set_ylabel("X2")
ax.set_zlabel("Y")
ax.set_xlim3d(-1, 1)
ax.set_ylim3d(-1, 1)
ax.set_zlim3d(-1, 1)
plt.title("Learned Model 3D", fontsize=12)
savefig("scatterdata3D.png", format="png")
plt.close()
#
# Compare to ground truth
#
print("trainsize " + str(trainsize))
Ytruth = Y[-trainsize:]
print("Ytruth.shape " + str(Ytruth.shape))
Xtest = dataX[-trainsize:, :]
print("Xtest.shape " + str(Xtest.shape))
Ytest = learner.query(Xtest) # to check every point
print("Ytest.shape " + str(Ytest.shape))
plt.clf()
plt.scatter(Ytruth, Ytest, edgecolors="none")
plt.xlim(-1.2, 1.2) # set x scale
plt.ylim(-1.2, 1.2) # set y scale
plt.xlabel("Ground Truth")
plt.ylabel("Estimated")
savefig("scatterdata.png", format="png")
print(corrcoef(Ytruth, Ytest))
