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# MxN matrix A and N sized vector b. Ab = 

# [1, 0, 3]
# [0, 0, 0]
# [0, 2, 4]

# [1, 2, 3]

import numpy as np
cols = np.array([0, 2, 1, 2])

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import numpy as np
from lightfm import LightFM
from sklearn.metrics import roc_auc_score
from lightfm.datasets import fetch_movielens

def auc_score(model, ratings):
    """
    computes area under the ROC curve (AUC).
    The full name should probably be mean
    auc score as it is computing the auc

ethen8181

import numpy as np
from tqdm import trange
from scipy.special import expit
from sklearn.base import BaseEstimator
from copy import deepcopy


class NeuralNet(BaseEstimator):
    """
    Neural Network for classification

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from hw1_nnet import NeuralNet
from keras.datasets.mnist import load_data

(X_train, y_train), (X_test, y_test) = load_data()
X_train = X_train.reshape((X_train.shape[0], -1)) / 255.0
X_test = X_test.reshape((X_test.shape[0], -1)) / 255.0


nn_params = {
    'reg': 0.01,

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import os
import numpy as np
from tqdm import trange
from scipy.special import expit
import matplotlib.pyplot as plt
from sklearn.base import BaseEstimator
from sklearn.metrics import accuracy_score

class NeuralNet(BaseEstimator):
    """

ethen8181

• brew install mvnvm (just to install maven on mac)
• make a eclipse maven project on your local (File -> New -> Project -> Maven Project). During the setup just click next until you run into a place that prompt you to set the group id = com.javamakeuse.hadoop.poc (it turns out you can name it whatever you want), artifact id = Homeworkx (name is whatever you want, e.g. Homework1)
• copy the pom.xml from wolf and replace the local pom.xml (you'll see it on your left in eclipse)
• go to src/main/java and start a new class (e.g. Exercise1) to do your coding
• after we're done coding, navigate to where the maven project is stored (e.g. mine is stored under /Users/ethen/Documents/workspace/Homework1) and type mvn package to create the jar file
• After that copy the mr-app-1.0-SNAPSHOT.jar inside the target folder to wolf.
• Then ssh to wolf and run the job on wolf using hadoop jar e.g. for the wordcount example I had a folder

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"""
python3

numpy>=1.12.0
pandas>=0.19.2
matplotlib>=2.0.0
sklearn>=0.18
xgboost>=0.6
sortedcontainers>=1.5.7
"""

ethen8181

import os
import math
import numpy as np
import pandas as pd
from PIL import Image
from collections import namedtuple

def preprocessing(folder):
    '''read data and obtain point cloud and camera center.'''
    filename = os.path.join(folder, 'final_project_point_cloud.csv')