This simple code that simply tries to replace semicolons (at i-specified postions) by colons does not work:
for i in range(0,len(line)): if (line==";" and i in rightindexarray):... moreThis simple code that simply tries to replace semicolons (at i-specified postions) by colons does not work:
for i in range(0,len(line)): if (line==";" and i in rightindexarray): line=":"
It gives the error
line=":" TypeError: 'str' object does not support item assignment
How can I work around this to replace the semicolons by colons? Using replace does not work as that function takes no index- there might be some semicolons I do not want to replace.
Example
In the string I might have any number of semicolons, eg "Hei der! ; Hello there ;!;"
I know which ones I want to replace (I have their index in the string). Using replace does not work as I'm not able to use an index with it. less
How can I call a Python function with my Node.js (express) backend server?
I want to call this function and give it an image url
def predictImage(img_path): # load model model =... moreHow can I call a Python function with my Node.js (express) backend server?
I want to call this function and give it an image url
def predictImage(img_path): # load model model = load_model("model.h5") # load a single image new_image = load_image(img_path) # check prediction pred = model.predict(new_image) return str(pred)
I have tried to puzzle out an answer to this question for many months while learning pandas. I use SAS for my day-to-day work and it is great for it's out-of-core support.... moreI have tried to puzzle out an answer to this question for many months while learning pandas. I use SAS for my day-to-day work and it is great for it's out-of-core support. However, SAS is horrible as a piece of software for numerous other reasons.
One day I hope to replace my use of SAS with python and pandas, but I currently lack an out-of-core workflow for large datasets. I'm not talking about "big data" that requires a distributed network, but rather files too large to fit in memory but small enough to fit on a hard-drive.
My first thought is to use HDF store to hold large datasets on disk and pull only the pieces I need into dataframes for analysis. Others have mentioned MongoDB as an easier to use alternative. My question is this:
What are some best-practice workflows for accomplishing the following:
Loading flat files into a permanent, on-disk database structure
Querying that database to retrieve data to feed into a pandas data structure
Updating the database after manipulating pieces in... less
I'm trying to understand the backpropagation algorithm with an XOR neural network as an example. For this case there are 2 input neurons + 1 bias, 2 neurons in the hidden layer +... moreI'm trying to understand the backpropagation algorithm with an XOR neural network as an example. For this case there are 2 input neurons + 1 bias, 2 neurons in the hidden layer + 1 bias, and 1 output neuron.
A B A XOR B 1 1 -1 1 -1 1 -1 1 1 -1 -1 -1
(source: wikimedia.org)
I'm using stochastic backpropagation.
After reading a bit more I have found out that the error of the output unit is propagated to the hidden layers... initially this was confusing, because when you get to the input layer of the neural network, then each neuron gets an error adjustment from both of the neurons in the hidden layer. In particular, the way the error is distributed is difficult to grasp at first.
Step 1 calculate the output for each instance of input.Step 2 calculate the error between the output neuron(s) (in our case there is only one) and the target value(s):Step 3 we use the error from Step 2 to calculate the error for each hidden unit h:
The 'weight kh' is the weight between the hidden unit h and the output... less
Can any one tell my what that part (town = thisLine)exactly do?
def get_list_of_university_towns():
'''Returns a DataFrame of towns and the states they are in from the... moreCan any one tell my what that part (town = thisLine)exactly do?
def get_list_of_university_towns():
'''Returns a DataFrame of towns and the states they are in from the
university_towns.txt list. The format of the DataFrame should be:
DataFrame( [ , ,
columns= )
The following cleaning needs to be done:
1. For "State", removing characters from "[" to the end.
2. For "RegionName", when applicable, removing every character from " (" to the end.
3. Depending on how you read the data, you may need to remove newline character '\n'. '''
data =
state = None
state_towns =
with open('university_towns.txt') as file:
for line in file:
thisLine = line
if thisLine == '':
state = thisLine
continue
if '(' in line:
town = thisLine
state_towns.append()
else:
town = thisLine
state_towns.append()
data.append(thisLine)
df = pd.DataFrame(state_towns,columns = )
return df
get_list_of_university_towns() less
Currently I use the following code:
callbacks =
model.fit(X_train.astype('float32'), Y_train, batch_size=batch_size, nb_epoch=nb_epoch,
shuffle=True, verbose=1,... moreCurrently I use the following code:
callbacks =
model.fit(X_train.astype('float32'), Y_train, batch_size=batch_size, nb_epoch=nb_epoch,
shuffle=True, verbose=1, validation_data=(X_valid, Y_valid),
callbacks=callbacks)
It tells Keras to stop training when loss didn't improve for 2 epochs. But I want to stop training after loss became smaller than some constant "THR":
if val_loss < THR:
break
I have a large set of vectors in 3 dimensions. I need to cluster these based on Euclidean distance such that all the vectors in any particular cluster have a Euclidean distance... moreI have a large set of vectors in 3 dimensions. I need to cluster these based on Euclidean distance such that all the vectors in any particular cluster have a Euclidean distance between each other less than a threshold "T".
I do not know how many clusters exist. At the end, there may be individual vectors existing that are not part of any cluster because its euclidean distance is not less than "T" with any of the vectors in the space.
What existing algorithms / approach should be used here?
Right now I'm importing a fairly large CSV as a dataframe every time I run the script. Is there a good solution for keeping that dataframe constantly available in between runs... moreRight now I'm importing a fairly large CSV as a dataframe every time I run the script. Is there a good solution for keeping that dataframe constantly available in between runs so I don't have to spend all that time waiting for the script to run?
I'm interesting in getting the connection from Python to machine learning part of OpenCV 2.2. OpenCV 2.2 already includes python bindings but only to the computer vision (cv) part... moreI'm interesting in getting the connection from Python to machine learning part of OpenCV 2.2. OpenCV 2.2 already includes python bindings but only to the computer vision (cv) part of it and not to the machine learning (ml) part.
Where could I get some third party bindings to also have access to the machine learning part
Can someone explain to me what is the purpose of meshgrid function in Numpy? I know it creates some kind of grid of coordinates for plotting, but I can't really see the direct... moreCan someone explain to me what is the purpose of meshgrid function in Numpy? I know it creates some kind of grid of coordinates for plotting, but I can't really see the direct benefit of it.I am studying "Python Machine Learning" from Sebastian Raschka, and he is using it for plotting the decision borders. See input 11 here.I have also tried this code from official documentation, but, again, the output doesn't really make sense to me.
x = np.arange(-5, 5, 1)
y = np.arange(-5, 5, 1)
xx, yy = np.meshgrid(x, y, sparse=True)
z = np.sin(xx**2 + yy**2) / (xx**2 + yy**2)
h = plt.contourf(x,y,z)
Please, if possible, also show me a lot of real-world examples.
Is it possible to delete or insert a step in a sklearn.pipeline.Pipeline object?I am trying to do a grid search with or without one step in the Pipeline object. And wondering... moreIs it possible to delete or insert a step in a sklearn.pipeline.Pipeline object?I am trying to do a grid search with or without one step in the Pipeline object. And wondering whether I can insert or delete a step in the pipeline. I saw in the Pipeline source code, there is a self.steps object holding all the steps. We can get the steps by named_steps(). Before modifying it, I want to make sure, I do not cause unexpected effects.Here is a example code:
from sklearn.pipeline import Pipeline
from sklearn.svm import SVC
from sklearn.decomposition import PCA
estimators =
clf = Pipeline(estimators)
clf
Is it possible that we do something like steps = clf.named_steps(), then insert or delete in this list? Does this cause undesired effect on the clf object? less
I am trying to follow this tutorial: https://medium.com/@natu.neeraj/training-a-keras-model-on-google-cloud-ml-cb831341c196to upload and train a Keras model on Google Cloud... moreI am trying to follow this tutorial: https://medium.com/@natu.neeraj/training-a-keras-model-on-google-cloud-ml-cb831341c196to upload and train a Keras model on Google Cloud Platform, but I can't get it to work.Right now I have downloaded the package from GitHub, and I have created a cloud environment with AI-Platform and a bucket for storage.I am uploading the files (with the suggested folder structure) to my Cloud Storage bucket (basically to the root of my storage), and then trying the following command in the cloud terminal:
gcloud ai-platform jobs submit training JOB1
--module-name=trainer.cnn_with_keras
--package-path=./trainer
--job-dir=gs://mykerasstorage
--region=europe-north1
--config=gs://mykerasstorage/trainer/cloudml-gpu.yaml
But I get errors, first the cloudml-gpu.yaml file can't be found, it says "no such folder or file", and trying to just remove it, I get errors because it says the --init--.py file is missing, but it isn't, even if it is empty (which it... less
