charanpald

# Run some recommendation experiments using MovieLens 100K
import pandas
import numpy
import scipy.sparse
import scipy.sparse.linalg
import matplotlib.pyplot as plt
from sklearn.metrics import mean_absolute_error

data_dir = "data/ml-100k/"
data_shape = (943, 1682)

charanpald

import numpy
import pandas
from sklearn.datasets import load_digits
from sklearn import preprocessing
from sklearn.cross_validation import KFold
from sklearn.svm import SVC
from sklearn.metrics import zero_one_loss
from keras.models import Sequential
from keras.layers.core import Dense, Activation

charanpald

import numpy
from sklearn.datasets import load_iris
from sklearn import preprocessing
from sklearn.decomposition import PCA
from sklearn.cross_decomposition import PLSRegression
from sklearn.cross_validation import KFold
from sklearn.svm import LinearSVC
from sklearn.metrics import zero_one_loss

dataset = load_iris()

charanpald

import pandas
import numpy
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import confusion_matrix, zero_one_loss

# Must declare data_dir as the directory of training and test files

train_data = data_dir + "kddcup.data.corrected"
train_labels = data_dir + "train_labels.txt"
test_data = data_dir + "corrected"