class Matrix:
def __init__(self, nr, nc):
self.NRows = nr
self.NCols = nc
self.data = [ [0]*self.NCols for r in range(self.NRows) ]
def max(self, other):
""" return: a matrix with as many rows as the shorter of self and other and as many columns as the narrower of self and other.
Each entry of the returned matrix should be the larger (the max) of the corresponding entries in self and other.
"""
minrows = min(other.NRows, self.NRows)
mincols = min(other.NCols, self.NCols)
M = Matrix(minrows, mincols)
for i in range(minrows):
for j in range(mincols):
M.data[i][j] = max(self.data[i][j], other[i][j])
return M
This code give Traceback in max when tested and output said:
...in max
M.data[i][j] = max(self.data[i][j], other[i][j])
AttributeError: Matrix instance has no attribute '__getitem__'
How to get rid of this error? Where I made mistake?. Please help someone.
You should use this :
M.data[i][j] = max(self.data[i][j], other.data[i][j])
instead of
M.data[i][j] = max(self.data[i][j], other[i][j])
Related
I am trying to do sequential feature selection in Python and would like to select the number of features through GridsearchCV, can someone help me with understanding why this happens?
X_SFS=X_train_normed
numbers = [x for x in range(1,19)]
svc = SVC(kernel='rbf', gamma=0.1, C = 10)
sfs = SequentialFeatureSelector(estimator=svc)
param_grid = [{'k_features':numbers}]
search = GridSearchCV(sfs, param_grid, cv=StratifiedKFold(5, shuffle=False), scoring='f1_macro')
search.fit(X_SFS, y_train)
svm_params.append(search.best_params_)
features_bool.append(search.best_estimator_.named_steps['sfs'].get_support())
scores.append(search.best_score_)
The above throws the error:
AttributeError: 'SequentialFeatureSelector' object has no attribute 'predict'
I'm writing a function that computes the sparsity of the weight matrices of the following fully connected network:
class FCN(nn.Module):
def __init__(self):
super(FCN, self).__init__()
self.fc1 = nn.Linear(input_dim, hidden_dim)
self.relu1 = nn.ReLU()
self.fc2 = nn.Linear(hidden_dim, hidden_dim)
self.relu2 = nn.ReLU()
self.fc3 = nn.Linear(hidden_dim, hidden_dim)
self.relu3 = nn.ReLU()
self.fc4 = nn.Linear(hidden_dim, output_dim)
def forward(self, x):
out = self.fc1(x)
out = self.relu1(out)
out = self.fc2(out)
out = self.relu2(out)
out = self.fc3(out)
out = self.relu3(out)
out = self.fc4(out)
return out
The function I have written is the following:
def print_layer_sparsity(model):
for name,module in model.named_modules():
if 'fc' in name:
zeros = 100. * float(torch.sum(model.name.weight == 0))
tot = float(model.name.weight.nelement())
print("Sparsity in {}.weight: {:.2f}%".format(name, zeros/tot))
But it gives me the following error:
torch.nn.modules.module.ModuleAttributeError: 'FCN' object has no attribute 'name'
It works fine when I manually enter the name of the layers (e.g.,
(model.fc1.weight == 0)
(model.fc2.weight == 0)
(model.fc3.weight == 0) ....
but I'd like to make it independent from the network. In other words, I'd like to adapt my function in a way that, given any sparse network, it prints the sparsity of every layer. Any suggestions?
Thanks!!
Try:
getattr(model, name).weight
In place of
model.name.weight
Your print_layer_sparsity function becomes:
def print_layer_sparsity(model):
for name,module in model.named_modules():
if 'fc' in name:
zeros = 100. * float(torch.sum(getattr(model, name).weight == 0))
tot = float(getattr(model, name).weight.nelement())
print("Sparsity in {}.weight: {:.2f}%".format(name, zeros/tot))
You can't do model.name because name is a str. The in-built getattr function allows you to get the member variables / attributes of an object using its name as a string.
For more information, checkout this answer.
Hello I am learning MDAnalysis through python-3.7. Would you please check my code and advise how to resolve the following error:
Traceback (most recent call last):
File "/home/pulokdeb/projects/def-sohrabz/pulokdeb/beluga_python/Closest_atom_Oxy_group.py", line 242, in <module>
eigen_value = iio.eigen_vals()
File "/home/pulokdeb/ENV/lib/python3.7/site-packages/MDAnalysis/core/topologyattrs.py", line 1347, in eigen_vals
com = atomgroup.center_of_mass(pbc=pbc)
NameError: name 'pbc' is not defined
The code (partial) is below:
def radius_of_gyration(group, pbc=False, **kwargs):
"""Radius of gyration.
Parameters
----------
pbc : bool, optional
If ``True``, move all atoms within the primary unit cell before
calculation. [``False``]
.. versionchanged:: 0.8 Added *pbc* keyword
"""
atomgroup = group.atoms
masses = atomgroup.masses
com = atomgroup.center_of_mass(pbc=pbc)
if pbc:
recenteredpos = atomgroup.pack_into_box(inplace=False) - com
else:
recenteredpos = atomgroup.positions - com
rog_sq = np.sum(masses * np.sum(recenteredpos**2,
axis=1)) / atomgroup.total_mass()
return np.sqrt(rog_sq)
transplants[GroupBase].append(
('radius_of_gyration', radius_of_gyration))
I changed a few lines (def_eif_vals) in topologyattrs.py file and got my results. Hope it works for my future simulations.
def shape_parameter(group, pbc=False, **kwargs):
"""Shape parameter.
See [Dima2004a]_ for background information.
Parameters
----------
pbc : bool, optional
If ``True``, move all atoms within the primary unit cell before
calculation. [``False``]
References
----------
.. [Dima2004a] Dima, R. I., & Thirumalai, D. (2004). Asymmetry
in the shapes of folded and denatured states of
proteins. *J Phys Chem B*, 108(21),
6564-6570. doi:`10.1021/jp037128y
<https://doi.org/10.1021/jp037128y>`_
.. versionadded:: 0.7.7
.. versionchanged:: 0.8 Added *pbc* keyword
"""
atomgroup = group.atoms
masses = atomgroup.masses
com = atomgroup.center_of_mass(pbc=pbc)
if pbc:
recenteredpos = atomgroup.pack_into_box(inplace=False) - com
else:
recenteredpos = atomgroup.positions - com
tensor = np.zeros((3, 3))
for x in range(recenteredpos.shape[0]):
tensor += masses[x] * np.outer(recenteredpos[x, :],
recenteredpos[x, :])
tensor /= atomgroup.total_mass()
eig_vals = np.linalg.eigvalsh(tensor)
shape = 27.0 * np.prod(eig_vals - np.mean(eig_vals)
) / np.power(np.sum(eig_vals), 3)
return shape
transplants[GroupBase].append(
('shape_parameter', shape_parameter))
def eigen_vals(group, pbc=False, **kwargs):
""" Changed by Pulok Deb
"""
atomgroup = group.atoms
masses = atomgroup.masses
com = atomgroup.center_of_mass(pbc=pbc)
if pbc:
recenteredpos = atomgroup.pack_into_box(inplace=False) - com
else:
recenteredpos = atomgroup.positions - com
tensor = np.zeros((3, 3))
for x in range(recenteredpos.shape[0]):
tensor += masses[x] * np.outer(recenteredpos[x, :],
recenteredpos[x, :])
tensor /= atomgroup.total_mass()
eig_vals = np.linalg.eigvalsh(tensor)
return eig_vals
transplants[GroupBase].append(
('eigen_vals', eigen_vals))
#warn_if_not_unique
#check_pbc_and_unwrap
I found a code online for Naive bayes classification for a small research I am doing. The code I am using is showing some errors and cannot find the solution for them. I would greatly appreciate your help.
The code is below:
# Example of Naive Bayes implemented from Scratch in Python
import csv
import random
import math
def loadCsv(filename):
lines = csv.reader(open(filename, "rt"))
dataset = list(lines)
for i in range(len(dataset)):
dataset[i] = [float(x) for x in dataset[i]]
return dataset
def splitDataset(dataset, splitRatio):
trainSize = int(len(dataset) * splitRatio)
trainSet = []
copy = list(dataset)
while len(trainSet) < trainSize:
index = random.randrange(len(copy))
trainSet.append(copy.pop(index))
return [trainSet, copy]
def separateByClass(dataset):
separated = {}
for i in range(len(dataset)):
vector = dataset[i]
if (vector[-1] not in separated):
separated[vector[-1]] = []
separated[vector[-1]].append(vector)
return separated
def mean(numbers):
return sum(numbers) / float(len(numbers))
def stdev(numbers):
avg = mean(numbers)
variance = sum([pow(x - avg, 2) for x in numbers]) / float(len(numbers) - 1)
return math.sqrt(variance)
def summarize(dataset):
summaries = [(mean(attribute), stdev(attribute)) for attribute in zip(*dataset)]
del summaries[-1]
return summaries
def summarizeByClass(dataset):
separated = separateByClass(dataset)
summaries = {}
for classValue, instances in separated.items():
summaries[classValue] = summarize(instances)
return summaries
def calculateProbability(x, mean, stdev):
exponent = math.exp(-(math.pow(x - mean, 2) / (2 * math.pow(stdev, 2))))
return (1 / (math.sqrt(2 * math.pi) * stdev)) * exponent
def calculateClassProbabilities(summaries, inputVector):
probabilities = {}
for classValue, classSummaries in summaries.items():
probabilities[classValue] = 1
for i in range(len(classSummaries)):
mean, stdev = classSummaries[i]
x = inputVector[i]
probabilities[classValue] *= calculateProbability(x, mean, stdev)
return probabilities
def predict(summaries, inputVector):
probabilities = calculateClassProbabilities(summaries, inputVector)
bestLabel, bestProb = None, -1
for classValue, probability in probabilities.items():
if bestLabel is None or probability > bestProb:
bestProb = probability
bestLabel = classValue
return bestLabel
def getPredictions(summaries, testSet):
predictions = []
for i in range(len(testSet)):
result = predict(summaries, testSet[i])
predictions.append(result)
return predictions
def getAccuracy(testSet, predictions):
correct = 0
for i in range(len(testSet)):
if testSet[i][-1] == predictions[i]:
correct += 1
return (correct / float(len(testSet))) * 100.0
def main():
filename = 'E:\iris.data.csv'
splitRatio = 0.67
dataset = loadCsv(filename)
trainingSet, testSet = splitDataset(dataset, splitRatio)
print(('Split {0} rows into train={1} and test={2} rows').format(len(dataset), len(trainingSet), len(testSet)))
# prepare model
summaries = summarizeByClass(trainingSet)
# test model
predictions = getPredictions(summaries, testSet)
accuracy = getAccuracy(testSet, predictions)
print(('Accuracy: {0}%').format(accuracy))
main()
The traceback for the same is below:
File "<ipython-input-18-4397d9969e66>", line 1, in <module>
runfile('C:/Users/Lenovo/Desktop/EE Codes/Knn with prima.py', wdir='C:/Users/Lenovo/Desktop/EE Codes')
File "C:\Users\Lenovo\Anaconda3\lib\site-packages\spyder\utils\site\sitecustomize.py", line 710, in runfile
execfile(filename, namespace)
File "C:\Users\Lenovo\Anaconda3\lib\site-packages\spyder\utils\site\sitecustomize.py", line 101, in execfile
exec(compile(f.read(), filename, 'exec'), namespace)
File "C:/Users/Lenovo/Desktop/EE Codes/Knn with prima.py", line 76, in <module>
main()
File "C:/Users/Lenovo/Desktop/EE Codes/Knn with prima.py", line 69, in main
neighbors = getNeighbors(trainingSet, testSet[x], k)
File "C:/Users/Lenovo/Desktop/EE Codes/Knn with prima.py", line 31, in getNeighbors
dist = euclideanDistance(testInstance, trainingSet[x], length)
File "C:/Users/Lenovo/Desktop/EE Codes/Knn with prima.py", line 24, in euclideanDistance
distance += pow((instance1[x] - instance2[x]), 2)
TypeError: unsupported operand type(s) for -: 'str' and 'str'
I would request you all to please provide a solution to how to solve this error for the respective code. If you require the dataset then please do ask. I can provide you the link for that too.
Thanks in advance
I am learning Bender's decomposition method and I want to use that in a small instance. I started from "bendersatsp.py" example in CPLEX. When I run this example with my problem, I got the following error. Could you please let me know what the problem is and how I can fix it? In the following you can see the modifications in lazy constraints function. I have two decision variables in master problem "z_{ik}" and "u_{k}" that would incorporate as constant in the workerLp.
class BendersLazyConsCallback(LazyConstraintCallback):
def __call__(self):
print("shoma")
v = self.v
u = self.u
z = self.z
print ("u:", u)
print ("z:", z)
workerLP = self.workerLP
boxty = len(u)
#scenarios=self.scenarios2
ite=len(z)
print ("ite:", ite)
print ("boxty:", boxty)
# Get the current x solution
sol1 = []
sol2 = []
sol3 = []
print("okkkk")
for k in range(1, boxty+1):
sol1.append([])
sol1[k-1]= [self.get_values(u[k-1])];
print ("sol1:", sol1[k-1])
for i in range(1, ite+1):
sol2.append([])
sol2[i-1]= self.get_values(z[i-1]);
print ("sol2:", sol2[i-1])
for i in range(1, ite+1):
sol3.append([])
sol3[i-1]= self.get_values(v[i-1]);
#print ("sol3:", sol3[i-1])
# Benders' cut separation
if workerLP.separate(sol3,sol1,sol2,v,u,z):
self.add(cut = workerLP.cutLhs, sense = "G", rhs = workerLP.cutRhs)
CPLEX Error 1006: Error during callback.
benders(sys.argv[1][0], datafile)
cpx.solve()
_proc.mipopt(self._env._e, self._lp)
check_status(env, status)
raise callback_exception
TypeError: unsupported operand type(s) for +: 'int' and 'list'