I have an Express Node.js application, but I also have a machine learning algorithm to use in Python. Is there a way I can call Python functions from my Node.js application to... moreI have an Express Node.js application, but I also have a machine learning algorithm to use in Python. Is there a way I can call Python functions from my Node.js application to make use of the power of machine learning libraries?
For now I'm using early stopping in Keras like this:
X,y= load_data('train_data') X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=12) datagen... moreFor now I'm using early stopping in Keras like this:
X,y= load_data('train_data') X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=12) datagen = ImageDataGenerator( horizontal_flip=True, vertical_flip=True) early_stopping_callback = EarlyStopping(monitor='val_loss', patience=epochs_to_wait_for_improve) history = model.fit_generator(datagen.flow(X_train, y_train, batch_size=batch_size), steps_per_epoch=len(X_train) / batch_size, validation_data=(X_test, y_test), epochs=n_epochs, callbacks=)
But at the end of model.fit_generator it will save model after epochs_to_wait_for_improve, but I want to save model with min val_loss does it make sense and is it possible? less
I'm trying to iterate over the words of a string.
The string can be assumed to be composed of words separated by whitespace.
Note that I'm not interested in C string functions or... moreI'm trying to iterate over the words of a string.
The string can be assumed to be composed of words separated by whitespace.
Note that I'm not interested in C string functions or that kind of character manipulation/access. Also, please give precedence to elegance over efficiency in your answer.
The best solution I have right now is:
#include <iostream> #include <sstream> #include <string> using namespace std; int main() { string s = "Somewhere down the road"; istringstream iss(s); do { string subs; iss >> subs; cout << "Substring: " << subs << endl; } while (iss); }
Is there a more elegant way to do this? less
I have created 2 python sets created from 2 different CSV files which contains some stings.
I am trying to match the 2 sets so that it will return an intersection of the 2 (the... moreI have created 2 python sets created from 2 different CSV files which contains some stings.
I am trying to match the 2 sets so that it will return an intersection of the 2 (the common strings from both the sets should be returned).
This is how my code looks:
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
import string
import nltk
#using content mmanager to open and read file
#converted the text file into csv file at the source using Notepad++
with open(r'skills.csv', 'r', encoding="utf-8-sig") as f:
myskills = f.readlines()
#converting mall the string in the list to lowercase
list_of_myskills = map(lambda x: x.lower(), myskills)
set_of_myskills = set(list_of_myskills)
#print(type(nodup_filtered_content))
print(set_of_myskills)
#open and read by line from the text file
with open(r'list_of_skills.csv', 'r') as f2:
#using readlines() instead of read(), becasue it reads line by line (each
line as a string obj in the python list)
contents_f2 =... less
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 HDFS 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 taking this course on Coursera, and I'm running some issues while doing the first assignment. The task is to basically use regular expression to get certain values from the... moreI'm taking this course on Coursera, and I'm running some issues while doing the first assignment. The task is to basically use regular expression to get certain values from the given file. Then, the function should output a dictionary containing these values:
example_dict = {"host":"146.204.224.152",
"user_name":"feest6811",
"time":"21/Jun/2019:15:45:24 -0700",
"request":"POST /incentivize HTTP/1.1"}
This is just a screenshot of the file. Due to some reasons, the link doesn't work if it's not open directly from Coursera. I apologize in advance for the bad formatting. One thing I must point out is that for some cases, as you can see in the first example, there's no username. Instead '-' is used.
159.253.153.40 - - "POST /e-business HTTP/1.0" 504 19845
136.195.158.6 - feeney9464 "HEAD /open-source/markets HTTP/2.0" 204 21149
This is what I currently have right now. However, the output is None. I guess there's something wrong in my... less
I tried to install XGBoost package in python. I am using windows os, 64bits . I have gone through following.
The package directory states that xgboost is unstable for windows and... moreI tried to install XGBoost package in python. I am using windows os, 64bits . I have gone through following.
The package directory states that xgboost is unstable for windows and is disabled: pip installation on windows is currently disabled for further invesigation, please install from github. https://pypi.python.org/pypi/xgboost/
I am not well versed in Visual Studio, facing problem building XGBoost. I am missing opportunities to utilize xgboost package in data science.
Please guide, so that I can import the XGBoost package in python.
Thanks
How do I save a trained Naive Bayes classifier to disk and use it... moreHow do I save a trained Naive Bayes classifier to disk and use it to predict data?
I have the following sample program from the scikit-learn website:
from sklearn import datasets
iris = datasets.load_iris()
from sklearn.naive_bayes import GaussianNB
gnb = GaussianNB()
y_pred = gnb.fit(iris.data, iris.target).predict(iris.data)
print "Number of mislabeled points : %d" % (iris.target != y_pred).sum()
Node.js is a perfect match for our web project, but there are few computational tasks for which we would prefer Python. We also already have a Python code for them. We are highly... moreNode.js is a perfect match for our web project, but there are few computational tasks for which we would prefer Python. We also already have a Python code for them. We are highly concerned about speed, what is the most elegant way how to call a Python "worker" from node.js in an asynchronous non-blocking way?
I want to get a list of the column headers from a Pandas DataFrame. The DataFrame will come from user input, so I won't know how many columns there will be or what they will be... moreI want to get a list of the column headers from a Pandas DataFrame. The DataFrame will come from user input, so I won't know how many columns there will be or what they will be called.
For example, if I'm given a DataFrame like this:
>>> my_dataframe y gdp cap 0 1 2 5 1 2 3 9 2 8 7 2 3 3 4 7 4 6 7 7 5 4 8 3 6 8 2 8 7 9 9 10 8 6 6 4 9 10 10 7
I would get a list like this:
>>> header_list
How is the convolution operation carried out when multiple channels are present at the input layer? (e.g. RGB)
After doing some reading on the architecture/implementation of a CNN... moreHow is the convolution operation carried out when multiple channels are present at the input layer? (e.g. RGB)
After doing some reading on the architecture/implementation of a CNN I understand that each neuron in a feature map references NxM pixels of an image as defined by the kernel size. Each pixel is then factored by the feature maps learned NxM weight set (the kernel/filter), summed, and input into an activation function. For a simple grey scale image, I imagine the operation would be something adhere to the following pseudo code:
for i in range(0, image_width-kernel_width+1):
for j in range(0, image_height-kernel_height+1):
for x in range(0, kernel_width):
for y in range(0, kernel_height):
sum += kernel * image
feature_map = act_func(sum)
sum = 0.0
However I don't understand how to extend this model to handle multiple channels. Are three separate weight sets required per feature map, shared between each colour?
Referencing this tutorial's... less
I have a dictionary: keys are strings, values are integers.Example:
stats = {'a':1000, 'b':3000, 'c': 100}
I'd like to get 'b' as an answer, since it's the key with a higher... moreI have a dictionary: keys are strings, values are integers.Example:
stats = {'a':1000, 'b':3000, 'c': 100}
I'd like to get 'b' as an answer, since it's the key with a higher value.I did the following, using an intermediate list with reversed key-value tuples:
inverse =
print max(inverse)
Is that one the better (or even more elegant) approach?
