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from numpy import matrix | |
matrix('1908 January 4.5 -5.6') | |
matrix("1908 'January' 4.5 -5.6") | |
matrix([4.5 -5.6],[2, 4]) | |
matrix([[4.5 -5.6],[2, 4]]) | |
matrix([[4.5 5.6],[2, 4]]) | |
matrix([[4.5, -5.6],[2, 4]]) | |
matrix([4.5, -5.6],[2, 4]) | |
matrix([[4.5, -5.6],[2, 4]]) | |
matrix([[4.5, -5.6],[2, 4]], header=['ss','b']) | |
matrix([[4.5, -5.6],[2, 4]], dtype=['ss','b']) | |
matrix([[4.5, -5.6],[2, 4]], dtype=('ss','b')) | |
import numpy | |
matrix([[4.5, -5.6],[2, 4]]) | |
matrix([[4.5, -5.6,6],[2, 4,5]]) | |
numpy.mat? | |
numpy.matrix? | |
matrix([[4.5, -5.6,6],[2, 4,5]]) | |
numpy.array([[4.5, -5.6,6],[2, 4,5]]) | |
x = numpy.array([[4.5, -5.6,6],[2, 4,5]]) | |
y = matrix([[4.5, -5.6,6],[2, 4,5]]) | |
y[0] | |
x[0] | |
numpy.array([[4.5, -5.6],[2, 4]], dtype=[('a','float'),('b','float')]) | |
numpy.array([(4.5, -5.6),(2, 4)] | |
, dtype=[('a','float'),('b','float')]) | |
numpy.array([(4.5, -5.6),(2, 4)], dtype=[('a','float'),('b','float')]) | |
numpy.array([(4.5, -5.6),(2, 4)], dtype=[('a','float'),('b','float')]) | |
g = numpy.array([(4.5, -5.6),(2, 4)], dtype=[('a','float'),('b','float')]) | |
g['a'] | |
g | |
numpy.hstack(g,[2,4]) | |
numpy.hstack(g) | |
numpy.hstack((g,[2,4])) | |
numpy.hstack((g,numpy.array([2,4]))) | |
numpy.array([2,4]) | |
g = numpy.array([(4.5, -5.6),(2, 4)], dtype=[('a','float'),('b','float')]) | |
f = numpy.array([(5, -6),(-2, 4)], dtype=[('a','float'),('b','float')]) | |
g | |
f | |
numpy.array(g,f) | |
numpy.hstack(g,f) | |
numpy.hstack((g,f)) | |
numpy.hstack((g,f))[0] | |
numpy.hstack((g,f))] | |
numpy.hstack((g,f))['a'] | |
numpy.array([('a', -6),('b', 4)], dtype=[('a','string'),('b','float')]) | |
numpy.array([('a', -6),('b', 4)], dtype=[('gg','string'),('b','float')]) | |
numpy.array([('a', -6),('b', 4)], dtype=[('gg','char'),('b','float')]) | |
numpy.array([('a', -6),('b', 4)], dtype=[('gg'),('b','float')]) | |
numpy.array([('a', -6),('b', 4)], dtype=[('gg','string;),('b','float')]) | |
numpy.array([('a', -6),('b', 4)], dtype=[('gg','string'),('b','float')]) | |
numpy.array([(a, -6),(b, 4)], dtype=[('gg','string'),('b','float')]) | |
numpy.array([('a', -6),('b', 4)], dtype=[('gg','string'),('b','float')]) | |
numpy.array([('a', -6),('b', 4)], dtype=[('gg','string'),('b','float')])[0] | |
numpy.array([('a', -6),('b', 4)], dtype=[('gg','object'),('b','float')])[0] | |
numpy.array([('a', -6),('b', 4)], dtype=[('gg','object'),('b','float')]) | |
numpy.array([('a', -6),('b', 4)], dtype=[('gg','object'),('b','float')])['gg'] | |
numpy.array([('a', -6),('b', 4)], dtype=[('gg','object'),('b','float')])['gg'][0] | |
numpy.array([('a', -6),('b', 4)], dtype=[('gg','object'),('b','float')])['gg'] | |
numpy.array([('a', -6),('b', 4)], dtype=object)['gg'] | |
numpy.array([('a', -6),('b', 4)], dtype=object) | |
numpy.array([('a', -6),('b', 4)], dtype=object)[0] | |
numpy.array([('a', -6),('b', 4)], dtype=[('gg','float64'),('b','float')])['gg'] | |
numpy.array([('a', -6),('b', 4)], dtype=[('gg','float64'),('b','float')])['gg'] | |
numpy.array([('a', -6),('b', 4)], dtype=[('gg','|S14'),('b','float')])['gg'] | |
numpy.array([('a', -6),('b', 4)], dtype=[('gg','|S14'),('ff','float')])['gg'] | |
numpy.array([('a', -6),('b', 4)], dtype=[('gg','|S10'),('ff','float')])['gg'] | |
numpy.array([('a', -6),('b', 4)], dtype=[('gg','|S10'),('ff','float')]) | |
numpy.array([('a', -6),('b', 4)], dtype=[('gg','|S10'),('ff','float')]) | |
numpy.array([('a', '-6'),('b', 4)], dtype=[('gg','|S10'),('ff','float')]) | |
numpy.array([('a', '-6', 3),('b', 4, 5)], dtype=[('gg','|S10'),('ff','float'), ('hh','float')]) | |
numpy.array([('a', '-6', 3),('b', 4, 5)], dtype=[('gg','|S10'),('ff','float'), ('hh','float')])['hh'] | |
ls | |
%load temperature_prediction.py | |
from itertools import combinations | |
from operator import and_ | |
import sys | |
from numpy import array, hstack | |
# libraries enabled: numpy, scipy, sklearn, nltk | |
# testcases/minima.txt | |
class PredictTemp: | |
''' | |
Apply AND operator on subsets with given conditions | |
''' | |
def __init__(self, size, heads, ip): | |
self.N = size | |
self.data = array(ip, dtype=[(heads[0],'int'), (heads[1],'|S10'), (heads[2], 'float'), (heads[3], 'float')]) | |
print self.data | |
def predict_already(self): | |
[self.subsets.extend(list(combinations(self.parent, x))) \ | |
for x in xrange(2, self.N + 1)] | |
#print self.subsets | |
[self.results.append(reduce(and_, subset)) \ | |
for subset in self.subsets] | |
return min(self.result) | |
if __name__ == '__main__': | |
N = int(raw_input()) | |
assert 1<=N<=1500 | |
COL_HEADS = raw_input() | |
ans = [] | |
ip = [] | |
for i in xrange(N): | |
temp = raw_input().split() | |
assert len(temp) == 4 | |
#temp[0], temp[2], temp[3] = int(temp[0]), float(temp[2]), float(temp[3]) | |
#assert 1908<=int(temp[0])<=2013 and -75<=int(temp[2])<=75 and -75<=int(temp[3])<=75 | |
ip.append(temp) | |
PT = PredictTemp(N, COL_HEADS, ip) | |
# ans.append(PT.predict_already()) | |
# for i in ans: print | |
PredictTemp.predict_already() | |
ip | |
array(ip, dtype=[(heads[0],'int'), (heads[1],'|S10'), (heads[2], 'float'), (heads[3], 'float')]) | |
heads = 'yyyy month tmax tmin'.split() | |
heads | |
array(ip, dtype=[(heads[0],'int'), (heads[1],'|S10'), (heads[2], 'float'), (heads[3], 'float')]) | |
ip | |
array(ip, dtype=[(heads[0],'int'), (heads[1],'|S10'), (heads[2], 'float'), (heads[3], 'float')]) | |
array(ip) | |
array(ip)[0] | |
x = array(ip) | |
x[:,] | |
x[:,0] | |
x[:,1] | |
x[:,2] | |
x[:,3] | |
x[:,4] | |
x[:,3] | |
x[:,2] | |
x[:,2] | |
x.reshape(2,2) | |
x.reshape(4,2) | |
x.reshape(2,4) | |
x.reshape(4) | |
x.reshape() | |
x[:,[1,2]] | |
x[:,[2,3]] | |
x[:4,[2,3]] | |
x[,[2,3]] | |
x[:,[2,3]] | |
print str(x) | |
x | |
x.clip(0,1) | |
x[:,[2,3]].clip(0,1) | |
x[:,[2,3]] | |
x = array(ip) | |
x | |
x[:,[2,3]].clip(0,1) | |
str(x[:,[2,3]]).clip(0,1) | |
x[:,[2,3]].clip(0,1) | |
x[:,[2,3]] | |
from sklearn.preprocessing import Imputer | |
x[:,[2,3]] | |
Imputer(x[:,[2,3]]) | |
Imputer(x) | |
x[:,[2,3]] | |
x | |
Imputer(x) | |
c = Imputer(x) | |
c.missing_values | |
Imputer? | |
Imputer(x[:,[2,3]]) | |
x | |
Imputer(x) | |
x.clip(0,1) | |
x.clip? | |
x[:,[2,3]].clip(0,1) | |
x[:,[2,3]].clip([0,1]) | |
x[:,[2,3]].clip(0,1) | |
x[:3,[2,3]].clip(0,1) | |
x[:3,[2,3]] | |
x[:3,[2,3]].clip(0,1) | |
from sklearn.ensemble import RandomForestRegressor | |
x.shape[0] | |
x | |
x.shape[1] | |
x.shape[2] | |
x.shape[2][0] | |
x.shape[2] | |
x[2].shape[2] | |
x | |
x[:3,[2,3]].shape[1] | |
x.shape? | |
from sklearn.cross_validation import cross_val_score | |
from sklearn.cross_validation import cross_val_score | |
from sklearn.datasets import load_boston | |
dataset = load_boston() | |
dataset | |
import numpy as np | |
from sklearn.datasets import load_boston | |
from sklearn.ensemble import RandomForestRegressor | |
from sklearn.pipeline import Pipeline | |
from sklearn.preprocessing import Imputer | |
from sklearn.cross_validation import cross_val_score | |
rng = np.random.RandomState(0) | |
rng | |
X_full, y_full = dataset.data, dataset.target | |
n_samples = X_full.shape[0] | |
n_features = X_full.shape[1] | |
X_full | |
n_ss | |
n_samples | |
%paste | |
missing_rate = 0.75 | |
n_missing_samples = np.floor(n_samples * missing_rate) | |
n_missing_samples | |
%paste | |
missing_samples | |
missing_features = rng.randint(0, n_features, n_missing_samples) | |
missing_features | |
%paste | |
score | |
import sklearn | |
sklearn.__version__ | |
%paste |
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