task_id
stringlengths 9
10
| prompt
stringlengths 133
1.16k
| canonical_solution
stringlengths 24
891
| entry_point
stringlengths 3
25
| test
stringlengths 3.51k
147M
|
|---|---|---|---|---|
EvoEval/0 |
from typing import List
def has_close_elements(numbers: List[float], threshold: float) -> bool:
""" Check if in given list of numbers, are any two adjacent numbers closer to each other than
given threshold.
>>> has_close_elements([1.0, 2.0, 3.0], 0.5)
False
>>> has_close_elements([1.0, 2.8, 3.0, 4.0, 5.0, 2.0], 0.3)
True
>>> has_close_elements([], 0.3)
False
"""
|
for i in range(len(numbers) - 1):
if abs(numbers[i + 1] - numbers[i]) < threshold:
return True
return False
| has_close_elements |
import numpy as np
from numpy import nan, inf # for some outputs.
import sys
sys.set_int_max_str_digits(0)
from enum import IntEnum, auto
def is_floats(x) -> bool:
# check if it is float; List[float]; Tuple[float]
# TODO: search for any close floats? (other data structures)
if isinstance(x, float):
return True
if isinstance(x, (list, tuple)):
return all(isinstance(i, float) for i in x)
if isinstance(x, np.ndarray):
return x.dtype == np.float64 or x.dtype == np.float32
return False
class DataType(IntEnum):
Float = auto()
Bool = auto()
Int = auto()
Str = auto()
Null = auto()
Tuple = auto()
List = auto()
Dict = auto()
Set = auto()
Type = auto()
Unknown = auto()
def get_type(x):
if x is None:
return DataType.Null
elif isinstance(x, bool):
return DataType.Bool
elif isinstance(x, int):
return DataType.Int
elif isinstance(x, str):
return DataType.Str
elif is_floats(x):
return DataType.Float
elif isinstance(x, tuple):
return DataType.Tuple
elif isinstance(x, list):
return DataType.List
elif isinstance(x, dict):
return DataType.Dict
elif isinstance(x, set):
return DataType.Set
elif isinstance(x, type):
return DataType.Type
else:
return DataType.Unknown
def is_equal(x, y) -> tuple[bool, str]:
x_type, y_type = get_type(x), get_type(y)
if x_type != y_type:
return False, "Type mismatch: {} vs {}".format(str(x_type), str(y_type))
if x_type in [DataType.Int, DataType.Bool, DataType.Null, DataType.Str, DataType.Set, DataType.Type]:
if x == y:
return True, None
try:
error_msg = "INT/BOOL/NULL/ Value mismatch: {} vs {}".format(repr(x)[:300], repr(y)[:300])
except:
error_msg = "Value mismatch: too large for display"
return False, error_msg
elif x_type == DataType.Float:
if np.allclose(x, y, equal_nan=True, atol=1e-6): # guard against nan
return True, None
else:
return False, "FLOAT Value mismatch: {} vs {}".format(x, y)
elif x_type in [DataType.List, DataType.Tuple]:
if len(x) != len(y):
return False, "Length mismatch: {} vs {}".format(len(x), len(y))
for i in range(len(x)):
equal, msg = is_equal(x[i], y[i])
if not equal:
return False, msg
return True, None
elif x_type == DataType.Dict:
if len(x) != len(y):
return False, "Length mismatch: {} vs {}".format(len(x), len(y))
for k, v in x.items():
if k not in y:
return False, "DICT Value mismatch: key {} in {} but not in {}".format(k, x, y)
equal, msg = is_equal(v, y[k])
if not equal:
return False, msg
return True, None
else:
try:
if x == y: # e.g., object comparison
return True, None
else:
return False, "ELSE Value mismatch: {} vs {}".format(x, y)
except:
return False, "Unsupported type: {} <-- {}".format(x_type, type(x))
def check(candidate):
inputs = [eval(f"[{i}]") for i in ['[1.0, 2.0, 3.9, 4.0, 5.0, 2.2], 0.3', '[1.0, 2.0, 3.9, 4.0, 5.0, 2.2], 0.05', '[1.0, 2.0, 5.9, 4.0, 5.0], 0.95', '[1.0, 2.0, 5.9, 4.0, 5.0], 0.8', '[1.0, 2.0, 3.0, 4.0, 5.0, 2.0], 0.1', '[1.1, 2.2, 3.1, 4.1, 5.1], 1.0', '[1.1, 2.2, 3.1, 4.1, 5.1], 0.5', '[1.0, 2.8, 4.0, 5.0, 1.2], 0.1', '[1.0, 2.8, 4.0, 5.0, 2.0], 0.5', '[], 0.3', '[1.0, 2.8, 3.0, 4.0, 5.0, 2.0], 0.3', '[1.0, 2.0, 3.0], 0.5', '[1.0, 2.5, 3.0, 4.5, 5.0, 6.5], 0.4', '[1.0, 2.0, 3.0, 2.9, 5.0, 6.0], 0.1', '[0.5, 0.6, 0.7, 0.8, 0.9], 0.05', '[10.5, 20.5, 30.5, 25.5, 40.5], 4.0', '[1.1, 2.2, 3.3, 4.4, 5.5, 6.6, 7.7], 0.2', '[0.1, 0.5, 1.0, 1.5, 2.0], 0.1', '[1.2, 2.4, 3.6, 4.8], 0.8', '[-1.0, -0.5, 0.0, 0.5, 1.0], 0.3', '[-2.0, 2.0, 3.0, 4.0], 1.5', '[1.0, 2.0, 3.0, 4.0, 5.0], 0.5', '[1.0, 2.0, 2.9, 5.0, 6.0], 4.4', '[1.0, 2.0, 3.0, 4.0, 5.0], 1.0', '[1.0, 2.0, 3.0, 4.0, 5.0, 4.0], 6.6', '[1.0, 2.5, 3.0, 4.5, 5.0, 6.5], 0.5837785211547324', '[1.0, 2.0, 3.0, 6.0], 1.4684959831900988', '[1.0, 2.0, 3.0, 4.0, 5.0, 4.0], 1.5', '[0.1, 0.5, 1.0, 1.5, 2.0], 5.0', '[1.0, 2.0, 3.0, 6.0], 0.5', '[1.0, 2.0, 3.0, 4.0, 5.0, 4.0, 3.1145064227603365], 1.5', '[1.0, 2.0, 3.0, 2.9, 5.0, 6.0, 6.6], 0.1', '[-2.0, 2.0, 3.0, 4.0], 1.7909669082553559', '[-1.0, -0.5, 0.0, 0.5, 1.0], 0.05', '[0.1, 0.5, 1.5, 2.0], 0.1', '[10.5, 20.5, 30.5, 25.5, 40.5, -1.0], 4.0', '[1.0, 1.4684959831900988, 3.0, 4.0, 5.0, 4.0], 6.6', '[1.0, 2.0, 3.0], 1.4684959831900988', '[1.0, 2.0, 3.0, 2.9, 5.0, 3.1145064227603365, 6.6], 0.001597775988153055', '[1.0, 2.0, 3.0, 4.0, 5.0, 4.0, 0.001597775988153055], 6.622770022559026', '[0.5, 0.7, 0.8, 0.9], 0.05', '[1.0, 2.0, 3.0, 4.0, 5.0], 1.4453152842907233', '[1.0, 2.5, 3.0, 4.5, 5.0, 6.5], 0.3', '[2.2, 3.3, 4.4, 5.5, 6.6, 7.7], 0.2', '[1.0, 3.0, 4.0, 5.0], 0.5', '[0.1, 0.5, 1.0, 1.5, 2.0, 0.5], 0.4', '[1.0, 2.0, 3.0, 2.9, 5.0, 6.0, 2.0, 2.0], 0.4395081641974091', '[1.0, 2.5, 3.0, 4.5, 6.5], 0.3', '[1.0, 2.0, 3.0, 4.0, 5.0, 2.4442803957785837, 1.0], 0.5', '[1.0, 3.0, 4.0, 5.0], 30.5', '[1.0, 2.5, 3.0, 4.5, 5.0, 5.40171670636793, 5.40171670636793], 0.3', '[0.5, 0.6, 0.7, 0.8], 3.1145064227603365', '[1.0, 2.0, 6.0], 1.4684959831900988', '[1.2, 2.4, 5.5, 4.8], 2.4', '[2.2, 3.3, 4.4, 5.5, 6.6, 7.7, 5.5], 0.2', '[1.0, 2.0, 3.0, 6.0], 0.4', '[-2.0, 3.0, 4.0], 1.7909669082553559', '[0.1, 0.5, 1.0, 1.5, 2.0], 0.4', '[0.1, 0.5, 1.0, 1.5, 2.0, 6.0], 4.029509770611085', '[1.2, 2.4, 6.622770022559026, 4.8], 3.0791117034371744', '[1.0, 2.5, 3.0, 4.5, 5.0, 6.5, 5.51097214523066], 0.5837785211547324', '[0.1, 0.5, 1.0, 1.5, 2.0], 4.8', '[1.0, 2.0, 3.0, 2.9, 5.0, 3.1145064227603365, 6.6], 25.5', '[1.0, 2.0, 3.0, 2.9, 5.0, 6.0, 2.0, 2.0], 10.5', '[1.0, 3.0], 4.28359856787508', '[1.0, 2.0, 3.0, 2.9, 6.0], 0.1', '[1.2, 2.4, 6.32960401348287, 4.8], 2.4', '[-2.0, 4.029509770611085, 3.0, 4.0], 1.5', '[1.2, 2.4, 6.32960401348287, 4.8], 2.5', '[1.0, 2.0, 3.0, 2.9, 5.0, 6.0, 2.0], 0.1', '[1.2, 2.4, 6.32960401348287, 4.8], 0.1', '[1.2, 2.4, 6.32960401348287, 4.220732489926661], 1.5434921314453987', '[-1.0, -0.5, 0.0, 0.5, 1.0], 3.0791117034371744', '[2.0, 3.0], 0.4', '[0.1, 0.5, 1.0, 1.5, 2.0, 0.5], 0.5872031050795127', '[10.5, 20.5, 30.5, 25.5, 40.5], 4.08640576656449', '[0.5, 0.7, 0.8, 0.9], 0.5956038469909051', '[1.0, 2.0, 3.0, 4.0, 5.0, 4.0, 3.1145064227603365, 3.7502832308571223], 1.5', '[-2.0, 2.0, 3.0, 4.0], 6.622770022559026', '[10.5, 30.5, 25.5, 40.5], 4.08640576656449', '[0.1, 0.5, 1.0, 1.5, 2.0], 0.5872031050795127', '[1.0, 2.0, 3.0, 4.0, 5.0, 4.0, 0.001597775988153055, 4.08640576656449], 6.622770022559026', '[1.0, 2.0, 3.0, 6.0, 2.0], 0.4', '[1.2, 2.4, 6.32960401348287, 4.8, 2.4], 2.4', '[1.2, 2.4, 6.622770022559026, 4.8], 9.342174793939517', '[2.4, 6.32960401348287, 4.8], 2.4', '[1.2, 2.4, 6.622770022559026, 4.8], 7.7', '[1.0, 9.342174793939517, 4.0, 5.0], 30.5', '[1.0, 2.0, 3.0, 4.0, 5.0], 6.622770022559026', '[0.1, 0.5, 1.0, 1.5, 2.0, 2.7536386742063454], 0.5872031050795127', '[0.5, 0.6, 0.7, 0.8, 0.5956038469909051], 7.7', '[-1.0, 1.0, 0.0, 0.5, 1.0], 0.3', '[1.0, 2.0, 3.0, 2.9, 5.0, 6.0, 2.0, 2.0], 5.5', '[1.0, 2.0, 3.0, 2.9, 5.0, 6.0, 2.0, 2.0], 0.46346457679116604', '[1.0, 2.5, 3.0, 5.0, 6.5, 0.4395081641974091], 0.5837785211547324', '[2.2, 3.3, 4.4, 5.5, 6.6, 7.7, 5.5], 3.3', '[-2.0, 2.0, 3.0, 4.0], 0.05', '[2.2, 3.3, 4.4, 5.5, 6.6, 7.7, 5.5], 0.22976043999045262', '[1.2, 2.4, 6.622770022559026, 4.8], 1.5434921314453987', '[1.2, 2.4, 6.622770022559026, 4.8], 1.6376315868318712', '[2.2, 3.3, 4.4, 5.5, 7.7, 5.5], 0.22976043999045262', '[1.0, 0.0, 0.5, 1.0], 0.3', '[1.2, 2.4, 6.32960401348287, 4.8], 0.22976043999045262', '[1.2, 2.4, 6.32960401348287, 4.8, 2.4], 0.22976043999045262', '[1.2, 2.4, 3.6, 4.8, 1.2], 0.8', '[1.0, 2.0, 3.0, 2.9, 5.0, 6.0], 1.2', '[1.0, 2.0, 3.0, 4.0, 5.0, 4.0, 3.1145064227603365, 3.7502832308571223, 4.814818917148268], 1.5', '[1.2, 2.4, 6.32960401348287, 4.8], 1.8783433029023973', '[1.2, 2.4, 6.32960401348287, 4.8, 4.8, 1.6616978677034633], 0.09078602525716209', '[1.0, 2.5, 3.0, 3.907675941633249, 5.0, 6.5, 4.5], 0.6417930585021959', '[1.0, 2.0, 3.0, 2.9, 5.0, 6.0, 2.518151204418766, 2.0], 2.0', '[-2.0, 4.029509770611085, 3.0, 4.0], 25.5', '[1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0], 1.0', '[0.0001, 0.0002, 0.0003, 0.0004, 0.0005], 1e-05', '[0.01, 0.02, 0.03, 0.04, 0.05], 0.001', '[1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0], 0.1', '[1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0], 1e-07', '[0.0, -1.0, 1.0, 2.0, 3.0, 4.0, 27.0, -3.5, 3.5], 0.5', '[0.5, 1.5, 2.5, 3.5, 4.5], 0.25', '[2.0, 4.0, 6.0, 8.0, 10.0], 0.4', '[0.01, 0.02, 0.03, 0.04, 0.05, 0.06], 0.001', '[1.0, 2.0, 2.9, 3.0, 3.1, 4.0, 5.0, 6.0], 0.2', '[1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 1.0], 1.0', '[0.0, -1.0, 1.0, 2.0, 3.0, 4.0, 27.0, -3.5, 3.5], 0.0002', '[1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0], 0.01', '[2.0, 4.0, 6.0, 0.4, 10.0], 0.3534420658062394', '[1.0, 0.02, 3.0, 4.0, 5.0, 6.0, 7.0, 0.02], 0.01', '[0.5, 1.5, 2.5, 3.5, 4.5], 1e-05', '[0.01, 2.9, 0.03, 0.04, 0.05, 0.06], 0.001', '[1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0], 1.1411672170968274', '[2.0, 4.0, 6.0, 8.0, 10.0], 0.02', '[1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0], 0.0002', '[0.5, -1.0, 1.0, 2.0, 3.0, 4.0, 27.0, -3.5, 3.5], 0.7151289158513581', '[2.0, 4.0, 6.0, 8.0, 10.0], 4.5', '[1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 1.0], 0.5876482754436165', '[0.01, 2.9, 0.03, 0.04, 0.05, 0.06, 0.01], 0.001', '[0.01, 1e-07, 0.03, 0.04, 0.05, 0.06], 0.03', '[1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0], 0.05', '[0.01, 0.03, 0.04, 0.06], 0.001', '[0.01, 0.02, 0.03, 0.04, 0.05], 0.0009536407568491921', '[0.0001, 0.0002, 0.0003, 0.0004, 0.0005], 3.0', '[0.5, 1.5, 2.5, 3.5, 4.5], 10.0', '[1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 3.0], 0.0002', '[1.0, 2.0, 3.0, 4.0, 0.05, 5.0, 6.0, 7.0], 1.1411672170968274', '[0.01, 0.02, 0.03, 0.03791583967678999, 0.05, 0.06], 0.001', '[0.0, -1.0, 1.0, 2.0, 3.0, 4.0, 27.0, -3.5, 3.5], 0.5815567161870863', '[0.01, 2.9, 2.419821239840023, 0.03, 0.04, 0.05, 0.06], 0.001', '[0.01, 0.02, 0.03, 0.03791583967678999, 0.06], 0.001', '[0.0001, 0.0002, 0.0003, 0.0004, 0.0005], 3.496608712535931', '[1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 0.001, 7.0], 1e-07', '[1.0, 2.0, 3.0, 4.0, 0.05, 5.0, 6.0, 7.0], 0.01', '[0.0001, 0.0002, 0.0003, 0.0004, 0.0005, 0.0001], 3.0', '[0.0001, 0.0002, 0.0003, -0.27533868871906764, 0.0004, 0.0005], 3.0', '[5.0, 0.01, 0.02, 0.03, 0.03791583967678999, 0.05, 0.06], 0.001', '[0.01, 0.02, 0.03, 2.419821239840023, 0.04, 0.05], 0.4', '[1.0, 2.0, 2.9, 3.0, 3.1, 4.0, 5.0, 6.0], 0.7065364188537329', '[5.0, 0.01, 0.02, 0.03, 0.05, 0.06], 0.001', '[2.0, 4.0, 6.0, 5.4033516333681195, 0.4], 0.3534420658062394', '[0.0001, 0.0002, 0.0003, 0.0004, 0.0005], 0.03791583967678999', '[1.0, 2.0, 2.9, 3.0, 3.1, 4.0, 5.0, 6.0], 0.23476792155713733', '[0.01, 0.0005, 0.03, 0.04, 0.06], 0.001', '[2.0, 4.0, 6.0, 10.0], 0.02', '[1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 1.0], 1.4554927325266915', '[1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0], 0.5387982733401486', '[0.01, 0.02, 0.03, 0.03791583967678999, 0.06, 0.06], 0.001', '[1.0, 2.4342970911810413, 3.0, 4.0, 0.05, 5.0, 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0.0003, 0.0004, 0.0005, 0.0005], 4.5', '[0.0001, 0.0002, 0.0003, 10.360620825036374, 0.0004, 0.0005, 2.5, 0.0003, 10.360620825036374], 0.4', '[0.01, 0.02, 0.03, 0.7830519816499151, 0.04, 0.05, 0.06, 0.05, 0.01], 6.628754599790156', '[0.01, 0.02, 0.03, 0.04, 0.05, 0.7851321679403163, 0.05], 0.0007771726708529467', '[0.01, 0.02, 0.03, 0.04, 0.05, 0.05061822472212914, 0.06, 0.01], 0.9236125030833611', '[0.01, 0.03, 0.04], 2.293054850153645', '[1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 4.0, 3.0, 3.0, 7.0], 3.4789370733342704', '[1.0, 2.0, 3.0, 4.0, 5.0, 3.1, 6.0, 7.0], 1.0', '[1.0, 2.0, 3.0, 4.0, 5.0, 3.1, 6.0, 7.0], 1.0784243887241944', '[0.01, 0.02, 0.03, 0.04, 0.05, 0.06], 5.2880862432816444', '[0.01, 0.02, 3.1, 0.04, 0.05], 0.055920116891141534', '[5.8932460669743824e-05, 0.0002, 0.0003, 0.0004, 0.0005, 0.0005], 0.1884230532937705', '[0.01, 0.02, 0.03, 0.04, 2.121475900327617, 0.05, 0.06], 12.782206302056885', '[7.327698262552655e-05, 0.24915745094654063, -0.49573144003113767, 0.0003, 0.0004], 0.0008183165574820457', '[1.0, 0.06, 3.0, 4.0, 5.0, 7.0], 4.193063041333624', '[0.01, 0.02, 0.8648911287584085, 0.04, 0.05, 0.7851321679403163, 0.06, 0.8648911287584085], 0.9737832702287503', '[1.0, 3.4789370733342704, 2.0, 3.0, 4.0, 5.0, 3.1, 6.0, 7.0], 0.0129477300365736', '[0.01, 0.02, 0.03, 0.04, 0.05, 0.04], 0.0005172209537761298', '[0.0001, 0.0002, 0.0003, 0.0004, 0.0005], 0.10215110330888037', '[0.0, -1.0, 1.0, 2.0, 3.0, 4.0, 27.0, -3.5, 3.5], 3.80230126436164', '[2.0, 5.297027774100714, 4.0, 6.0, 8.0, 10.0, 2.0], 0.4194505759272462', '[0.8688009538391689, 0.0003, 0.24915745094654063, 0.0004, 0.0005], 2.1338227658168414', '[0.0001, 0.0003, 0.0003, 0.0004, 0.0005, 0.0005, 0.0001, 0.0003, 0.0003], 1.4084871604544225e-05', '[0.0, -1.0, 1.0, 2.0, 3.0, 4.0, 27.0, -3.5, 3.5], 0.3993502229658934', '[1.0, 2.0, 3.0, 4.0, 4.369218129512683, 3.610794371373272, 6.0, 7.0, 0.8688009538391689, 3.0, 3.2337339304353026, 6.0], 4.554151651658612', '[1.0, 2.0, 3.0, 4.0, 4.369218129512683, 6.0, 7.0, 4.0, 3.0, 3.0, 4.0], 3.4789370733342704', '[0.0, -1.0, 1.0, 2.0, 0.5, 3.0, 4.0, 27.0, -3.5, 3.5], 0.9164049075273353', '[0.01, 0.02, 0.0, 3.1, 0.04, 4.320267231190421], 0.4489610242046808', '[2.9, 0.0002, 0.0004], 0.06551718965331986', '[0.0001, 0.0002, 0.0003, 0.0004, 3.0, 0.0003], 5.932113294381724', '[0.027058224311019348, 0.0001, 0.0002, 0.0003, 4.320267231190421, 0.0004, 0.0005, 0.0005, 0.0004], 0.05', '[0.0001, 0.0002, 0.0003, 0.0004, 0.9236125030833611, 0.0005, 0.0005], 0.34173466739005054', '[0.01, 0.03, 0.04, 0.05, 0.06], 1.9255680498201777', '[1.0, 2.0, 3.0, 4.0, 5.0, 3.1, 6.0, 7.0], 0.7923707008984809', '[0.01, 0.03, 0.04, 0.05, 0.06, 0.04, 0.01], 0.24915745094654063', '[1.0, 2.0, 3.0, 4.0, 5.0, 7.0, 4.831203365732389, 1.0], 0.1', '[0.01, 0.03, 0.04, 0.05], 0.7083071514757247', '[0.0, -1.0, 1.0, 2.0, 3.0, 4.0, 27.0, -3.5, 3.5], 0.6732530490080072', '[5.6316080215521163e-05, 0.0002, 0.0003, 10.360620825036374, 0.0004, 0.0005, 2.5, 0.0003, 10.360620825036374], 0.000864368171540714', '[0.0, -1.0, 1.0, 2.0, 3.0, 4.0, 27.0, -3.5, 3.5, 4.0], 3.80230126436164', '[0.0001, 0.0002, 0.0003, 0.0004, 0.0005], 0.2366548975305903', '[0.01, 0.02, 0.03, 0.04, 0.05, 0.06], 3.80230126436164', '[2.546056560331472, 1.5, 2.5, 3.5, 4.5], 0.9748345950231323', '[0.0001, 4.0, 0.0004, 0.0005, 0.0005], 1.258526307128289e-05', '[0.5, 1.5, 0.0004849742100830799, 2.5, 3.5, 4.5], 0.05766460375946958', '[1.0, 2.0, 3.0, 4.0, 4.369218129512683, 6.0, 7.0, 4.0, 0.7690733466089933, 3.0], 10.360620825036374', '[1.0, 3.4789370733342704, 2.0, 3.0, 4.0, 5.0, 3.1, 6.0, 7.0], 0.4721319649371413', '[4.554151651658612, 0.02, 0.03, 0.04, 0.05], 3.307663458004549', '[0.0, -1.0, 1.0, 2.0, 3.0, 4.0, 27.0, -3.5, 3.5], 0.5803213720025598', '[0.0, -1.0, 1.0, 2.0, 3.0, 4.0, 27.0, -3.5, 3.5], 3.75736209595818', '[0.5, 0.7830519816499151, 1.5, 2.5, 3.5, 4.5], 2.9252594886571455', '[1.0, 2.0, 3.0, 4.0, 5.0, 7.0, 4.0, 3.0, 3.0, 3.0, 4.0], 4.35968221414135', '[2.0, 3.0, 0.2366548975305903, 5.932113294381724, 7.0, 5.932113294381724], 3.6353279062810095', '[0.0001, 0.0003, 0.0004, 0.0005, 0.0005, 0.0001, 0.0003, 0.0003, 0.0003], 1.4084871604544225e-05', '[0.0001, 0.0002, -0.49573144003113767, 0.0003, 0.0004], 0.008289598692317044', '[0.01, 0.02, 0.03, 0.04, 3.0036344781111617, 0.05, -0.15838574353129323, 0.06], 2.8139825387707576e-08', '[6.0, 1.0, 2.0, 3.0, 5.0, 6.0], 8.427615438201482', '[0.0001, 0.0002, 0.0003, 0.9236125030833611, 0.0005], 0.10215110330888037']]
outputs = [True, False, False, False, False, True, False, False, False, False, True, False, False, False, False, False, False, False, False, False, True, False, True, False, True, True, True, True, True, False, True, False, True, False, False, False, True, True, False, True, False, True, False, False, False, False, True, False, False, True, True, True, True, True, False, False, True, False, True, True, True, True, True, True, True, False, True, True, True, False, False, True, True, False, True, False, True, True, True, False, True, True, False, True, True, True, True, True, True, True, True, False, True, True, True, True, False, False, True, True, False, False, False, False, False, True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False, True, False, False, False, False, False, False, False, True, False, False, False, True, False, False, True, False, False, False, True, True, False, True, False, False, False, False, True, False, False, True, True, False, True, True, False, False, True, True, False, False, True, False, True, True, True, False, True, False, True, True, True, False, True, True, True, True, False, True, False, False, True, False, True, True, False, True, False, True, True, False, False, False, True, False, True, True, False, True, False, True, True, False, False, False, True, True, False, True, True, True, False, True, True, True, True, True, True, True, False, True, True, True, False, False, True, False, True, True, True, True, True, True, True, True, False, False, False, True, True, True, True, True, True, False, False, False, False, True, False, True, True, False, True, True, True, False, False, True, True, False, True, False, False, True, True, True, False, True, True, True, True, False, True, False, False, False, False, False, True, True, False, True, True, True, True, True, True, True, False, False, True, True, True, False, False, True, True, True, True, False, False, True, True, True, True, False, True, True, True, True, True, False, False, True, False, True, True, False, True, True, True, True, False, True, True, True, True, True, True, True, False, True, True, True, False, False, True, False, True, True, False, True, True, False, True, True, True, True, True, True, True, False, True, False, True, True, False, True, True, True, True, False, True, True, False, False, False, False, True, False, True, True, False, True, False, True, True, False, False, True, True, False, False, False, False, True, True, False, True, True, True, True, True, False, True, False, True, False, False, True, True, True, True, False, True, False, True, False, False, True, True, False, True, True, True, True, True, True, True, True, True, True, False, True, False, False, False, False, True, False, False, False, False, True, True, True, True, True, False, False, False, True, True, True, False, False, False, True, False, True, False, True, False, True, True, True, False, True, True, True, True, False, True, True, False, False, False, True, True, True, False, True, True, True, True, True, True, True, True, True, False, True, True, True, True, False, True, True, False, True, True, True, False, True, True, False, False, True, True, True, False, True, True, False, True, False, True, True, False, False, False, True, True, True, False, False, True, True, True, True, True, True, False, True, False, False, True, True, False, True, True, False, False, True, True, False, False, True, True, True, False, True, True, True, True, False, True, True, False, True, False, True, False, False, False, True, False, True, True, True, False, True, True, True, True, False, True, False, False, False, True, False, True, True, True, True, False, False, False, True, True, False, True, True, True, True, True, False, False, True, True, True, True, True, True, False, False, True, True, True, True, False, True, False, True, False, True, False, True, True, True, True, True, True, True, True, True, True, True, True, False, False, False, True, True, False, False, True, True, True, False, False, True, True, True, False, True, False, False, True, False, True, True, False, False, True, True, True, False, True, False, False, True, True, True, True, True, True, True, False, True, False, True, False, True, False, False, True, True, True, True, True, True, True, True, True, True, False, True, False, True, False, True, True, True, True, False, False, False, True, False, False, True, True, True, True, False, True, False, True, False, False, True, True, False, True, True, True, False, True, True, True, True, False, True, True, True, True, True, False, True, True, True, True, True, True, False, False, True, True, True, True, True, True, True, True, True, True, False, False, True, True, True, True, True, True, True, True, True, False, False, False, True, True, False, False, True, True, True, True, True, True, False, True, False, True, False, True, True, True, False, True, False, True, True, True, True, True, True, True, True, True, True, True, True, False, True, True, True, True, False, True, True, True, True, True, True, False, False, False, True, True, True, True, False, False, True, True, True, True, True, False, False, False, True, False, False, False, True, True, True, False, True, True, True, True, True, True, True, True, True, True, True, True, True, False, True, False, True, False, False, True, True, True, True, True, True, True, True, True, False, False, True, True, True, False, True, True, False, True, True, False, True, True, True, True, True, True, True, True, True, True, True, False, True, True, True, False, True, False, False, True, False, True, True, False, True, False, True, True, True, False, True, True, True, True, False, False, False, True, True, True, True, True, True, True, True, True, True, True, True, True, True, False, True, True, True, True, True, True, True, False, True, False, True, False, True, True, True, True, False, True, False, True, True, True, False, True, True, True, True, True, False, False, True, False, False, False, True, False, True, True, True, True, False, True, True, True, False, True, True, True, True, True, True, True, True, False, False, True, True, False, True, True, False, True, True, False, True, True, True, True, True, True, False, True, False, True, False, True, True, True, True, False, True, False, True, False, True, False, True, True, True, True, True, True, False, True, True]
for i, (inp, exp) in enumerate(zip(inputs, outputs)):
exact_match, _ = is_equal(candidate(*inp), exp)
assert exact_match
|
EvoEval/1 | "from typing import List\n\n\ndef separate_paren_groups(paren_string: str) -> List[str]:\n \"\"\"(...TRUNCATED) | "\n\n\n cnt, group, results = 0, \"\", []\n for ch in paren_string:\n if ch == \"(\": c(...TRUNCATED) | separate_paren_groups | "\nimport numpy as np\nfrom numpy import nan, inf # for some outputs.\nimport sys\n\nsys.set_int_max(...TRUNCATED) |
EvoEval/2 | "\ndef truncate_number(number: float) -> float:\n \"\"\" Given a positive floating point number, (...TRUNCATED) |
return int(number), number - int(number)
| truncate_number | "\nimport numpy as np\nfrom numpy import nan, inf # for some outputs.\nimport sys\n\nsys.set_int_max(...TRUNCATED) |
EvoEval/3 | "\nfrom typing import List\n\n\ndef below_one(operations: List[int]) -> bool:\n \"\"\" You're giv(...TRUNCATED) | "\n\n account = 1\n for operation in operations:\n account += operation\n if acc(...TRUNCATED) | below_one | "\nimport numpy as np\nfrom numpy import nan, inf # for some outputs.\nimport sys\n\nsys.set_int_max(...TRUNCATED) |
EvoEval/4 | "\nfrom typing import List\n\n\ndef median_absolute_deviation(numbers: List[float]) -> float:\n \(...TRUNCATED) | "\n def median(sorted_lst):\n return sorted_lst[len(sorted_lst) // 2] if len(sorted_lst) %(...TRUNCATED) | median_absolute_deviation | "\nimport numpy as np\nfrom numpy import nan, inf # for some outputs.\nimport sys\n\nsys.set_int_max(...TRUNCATED) |
EvoEval/5 | "\nfrom typing import List\n\n\ndef intersperse(numbers: List[int], delimeter: int) -> List[int]:\n (...TRUNCATED) | "\n\n\n\n res = []\n for i in range(len(numbers)):\n res.append(numbers[i])\n if(...TRUNCATED) | intersperse | "\nimport numpy as np\nfrom numpy import nan, inf # for some outputs.\nimport sys\n\nsys.set_int_max(...TRUNCATED) |
EvoEval/6 | "\nfrom typing import List\n\n\ndef parse_nested_parens(paren_string: str) -> List[int]:\n \"\"\"(...TRUNCATED) | "\n\n \n def count_depth(s: str) -> int:\n max_depth, cnt = 0, 0\n for ch in s:\(...TRUNCATED) | parse_nested_parens | "\nimport numpy as np\nfrom numpy import nan, inf # for some outputs.\nimport sys\n\nsys.set_int_max(...TRUNCATED) |
EvoEval/7 | "\nfrom typing import List\n\n\ndef filter_by_substring(strings: List[str], substring: str) -> List[(...TRUNCATED) |
return list(filter(lambda s: substring not in s, strings))
| filter_by_substring | "\nimport numpy as np\nfrom numpy import nan, inf # for some outputs.\nimport sys\n\nsys.set_int_max(...TRUNCATED) |
EvoEval/8 | "\nfrom typing import List, Tuple\n\n\ndef sum_product(numbers: List[int]) -> Tuple[int, int]:\n (...TRUNCATED) | "\n\n\n s, p = 0, 1\n for number in numbers:\n if number >= 0:\n s += number(...TRUNCATED) | sum_product | "\nimport numpy as np\nfrom numpy import nan, inf # for some outputs.\nimport sys\n\nsys.set_int_max(...TRUNCATED) |
EvoEval/9 | "\nfrom typing import List, Tuple\n\n\ndef rolling_min(numbers: List[int]) -> List[int]:\n \"\"\"(...TRUNCATED) |
return [min(numbers[:(i+1)]) for i in range(len(numbers))]
| rolling_min | "\nimport numpy as np\nfrom numpy import nan, inf # for some outputs.\nimport sys\n\nsys.set_int_max(...TRUNCATED) |
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