# coding: utf-8
from __future__ import print_function, division, unicode_literals
from toolz import pluck, compose
from tabulate import tabulate
from utility import csv_reader, Scaler, prepend_x0, dot
import logistic_regression as logr
import glm
from out_utils import logistic_table
from ml_util import train_test_split
# Get the iris data set
# SL: sepal length, SW: Sepal Width, PL: Petal Length, PW: Petal Width
# 0: Iris Setosa 1: Iris Versicolour 2: Iris Virginica
Z, q = csv_reader('./data/iris.csv', ['SL', 'SW', 'PL', 'PW'], 'Type')
# Get Sepal Length and Petal Length features
Zp = list(pluck([0, 2], Z))
# Get only the Iris Setosa (0) and Iris Versicolour (1) classes
datap = [[f, o] for f, o in zip(Zp, q) if o != 2.0]
Xp, yp = zip(*datap)
y = list(yp)
Xpp = [list(e) for e in Xp]
print(Xpp)
print(y)
# Split set into training and testing data
train_data, test_data = train_test_split(zip(Xpp, y), 0.33)
# Scale the data
X_train, y_train = zip(*train_data)
scale = Scaler()
scale.fit(X_train)
transform = compose(prepend_x0, scale.transform)
# coding: utf-8
from __future__ import print_function, division, unicode_literals
from functools import partial
from toolz import compose, identity
from tabulate import tabulate
from utility import csv_reader, Scaler, prepend_x0
import metrics
import linear_regression as lr
import glm
from ml_util import train_test_split
import numpy as np
from numpy.linalg import lstsq
# Get the data
Z, y = csv_reader('./data/Folds_small.csv', ['AT', 'V', 'AP', 'RH'], 'PE')
data = zip(Z, y)
# Split into a train set and test set
train_data, test_data = train_test_split(data, 0.33)
# Scale the training data
scale = Scaler()
Z_train, y_train = zip(*train_data)
scale.fit(Z_train)
transform = compose(prepend_x0, scale.transform)
X_train = transform(Z_train)
scaledtrain_data = zip(X_train, y_train)
# Scale the testing data using the same scaling parameters
# used for the training data
Z_test, y_test = zip(*test_data)
X_test = transform(Z_test)
h_theta0 = [0., 0., 0., 0., 0.]
# coding: utf-8
from __future__ import print_function, division, unicode_literals
from functools import partial
from toolz import compose, identity
from tabulate import tabulate
from utility import csv_reader, Scaler, prepend_x0
import metrics
import linear_regression as lr
import glm
from ml_util import train_test_split
import numpy as np
from numpy.linalg import lstsq
# Get the data
Z, y = csv_reader('./data/Folds_small.csv', ['AT', 'V', 'AP', 'RH'], 'PE')
data = zip(Z, y)
# Split into a train set and test set
train_data, test_data = train_test_split(data, 0.33)
# Scale the training data
scale = Scaler()
Z_train, y_train = zip(*train_data)
scale.fit(Z_train)
transform = compose(prepend_x0, scale.transform)
X_train = transform(Z_train)
scaledtrain_data = zip(X_train, y_train)
# Scale the testing data using the same scaling parameters
# used for the training data
Z_test, y_test = zip(*test_data)
X_test = transform(Z_test)
h_theta0 = [0., 0., 0., 0., 0.]
import random
import numpy as np
import matplotlib.pyplot as plt
import glm
import logreg as lr
from utility import csv_reader, Scaler
from ml_util import train_test_split, encode_labels
from out_utils import logistic_table
from metrics import MScores
# Get the iris data set
# SL: sepal length, SW: Sepal Width, PL: Petal Length, PW: Petal Width
# 0: Iris Setosa 1: Iris Versicolour 2: Iris Virginica
Z, q = csv_reader('./data/iris.csv', ['SL', 'SW', 'PL', 'PW'], 'Type')
t = list(zip(Z, q))
random.shuffle(t)
W, u = list(zip(*t))
yencoded = encode_labels(np.array(u), 3)
train_data, test_data = train_test_split(zip(W, yencoded), 0.33)
# Scale data
Z_train, y_train = zip(*train_data)
y_train = np.array(y_train)
scale = Scaler()
scale.fit(Z_train)
scaledX_train = scale.transform(Z_train)
Z_test, y_test = zip(*test_data)
y_test = np.array(y_test)
scaledX_test = scale.transform(Z_test)
2) checks over related cells
3) yeets values in anyway
"""
print(grid)
check_possible_values(grid)
compare_related_cells(grid)
quasi_guess(grid)
if not solved(grid):
print("couldn't completely solve Sudoku")
else:
print("Solved")
print(grid)
return
# use a decorator for this?
def sudoku_solver(grid, time_it=True):
if time:
start = time()
solve()
if time:
dt = time() - start
print("time taken = ", dt, "seconds")
return
if __name__ == "__main__":
grid = csv_reader(argv[1])
this_sudoku = SudokuGrid(grid)
solve(this_sudoku)
