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Home > Pre-Algebra calculators

 AtoZmath.com - Homework help (with all solution steps) Educational Level Secondary school, High school and College Program Purpose Provide step by step solutions of your problems using online calculators (online solvers) Problem Source Your textbook, etc

 Pre-Algebra Calculators 1. Addition, Subtraction, Multiplication, Division of two numbers 2. Pronounce a number 3. Factors of a number (Prime factors, All factors) 4. HCF and LCM of numbers 5. Prime Numbers 6. Simplifying Numerical Expressions 7. Decimal number 1. Decimal to Fraction conversion 2. Fraction to Decimal conversion 3. Round off decimal number 4. Decimal number To Scientific notation 5. Scientific notation To Decimal number 8. Fraction 1.1 Reduced Terms 1.2 Improper Fraction To Mixed Number 1.3 Fraction to Decimal 2. Mixed Number To Improper Fraction 3. Compare Two Fraction 4. Addition, Subtraction, Multiplication and Division of Fraction Numbers 9.1 Powers 1. Square of a number 2. Cube of a number 3. Nth Power of a number 9.2 Radicals 4. Square root of a number using prime factorization method 5. Cube root of a number using prime factorization method 6. Nth root of a number using prime factorization method 7. Square root of a number using long division method 8. Cube root of a number using long division method 9. Find the smallest number which must be Added to 180 to make it perfect Square 10. Find Least number of 4 digits which is a perfect Square 10. Divisibility test by 2,4,8, 3,9, 5,10, 7,11,13,17,19,23,29,31,37,41,43 11. Find the next term in the number series (1. 3, 6, 18, 72, ___ 2. 20, 19, 17, 14, 10, ___) 12. Unit Conversion 1. Angle to Degree Conversion 2. Degree Conversion 3. Length Conversion 4. Mass Conversion 5. Money Conversion 6. Power Conversion 7. Speed Conversion 8. Temperature Conversion 9. Time Conversion 10. Volume Conversion 13. One Base to Other Base Conversion 1. Decimal to Binary 2. Decimal to Octal 3. Decimal to Hexadecimal 4. Binary to Decimal 5. Binary to Octal 6. Binary to Hexadecimal 7. Octal to Decimal 8. Octal to Binary 9. Octal to Hexadecimal 10. Hexadecimal to Decimal 11. Hexadecimal to Binary 12. Hexadecimal to Octal 13. Decimal to Other 14. Other to Decimal 15. Other to Other 14. Tables 1. Addition table / Chart 2. Multiplication table / Chart 3. Roman table 4. Factorial table 5. Number system conversion table 6. Square table 7. Cube table 8. Power table 9. Sin table 10. Cos table 11. Tan table 12. Sin, Cos, Tan table 13. Area formula 14. Volume formula 15. Factors of an equation formula 15. Place Value 16. Leap Year
1. Addition, Subtraction, Multiplication, Division for Two numbers
1. Find addition of 2760 and 796
 1 1 2 7 6 0 + 0 7 9 6 3 5 5 6
2. Find Subtraction of 2760 and 796
 16 15 10 1 6 5 0 2 7 6 0 - 0 7 9 6 1 9 6 4
3. Find multiplication of 2760 and 796
 2760 × 796 1932000 248400 16560 2196960
4. Find division of 276 and 6

6 |
 2 7 6
| 46
|
 2 4

|

|
 3 6

|
 3 6

|

|
 0

Here, Divisor = 6
Dividend = 276
Quotient = 46
Remainder = 0
 2. Pronounce a number In Indian currency format and Other currency format. In Indian currency format 12345678 = One Crore Twenty Three Lakh Forty Five Thousand Six Hundred Seventy Eight In Other currency format 12345678 = Twelve Million Three Hundred Forty Five Thousand Six Hundred Seventy Eight

3. Factors of a number
1. All Possible Factors of a number
Eg: All Possible factors of 456 are as follows...
1, 2, 3, 4, 6, 8, 12, 19, 24, 38, 57, 76, 114, 152, 228, 456

2. All Prime Factors of a number
Eg: Find Prime factors of 456

 2 | 456 | 2 | 228 | 2 | 114 | 3 | 57 | 19 | 19 | | 1

Or

456
= 2 × 228
= 2 × 2 × 114
= 2 × 2 × 2 × 57
= 2 × 2 × 2 × 3 × 19

Or

Prime factors in exponential form
= 23 × 31 × 191

Or

Prime factorization tree

 456 /  \ 2 228 /  \ 2 114 /  \ 2 57 /  \ 3 19
4. HCF and LCM of Two / Multiple Numbers
1. Find HCF, LCM of 30,50
Factor of 30,50 are as follows...
30 = 2 × 3 × 5
50 = 2 × 5 × 5

HCF = 2 × 5 = 10
LCM = 2 × 3 × 5 × 5 = 150

2. Find HCF of 30,50 using Division Method...

Find HCF of (30,50)

 1

30|
 5 0

|
 3 0

|

|
 2 0

 1

20|
 3 0

|
 2 0

|

|
 1 0

 2

10|
 2 0

|
 2 0

|

|
 0

∴ HCF of given nos (30,50) = 10

3. Find LCM of 30,50 using Division Method...

 2 | 30 -- 50 | 3 | 15 -- 25 | 5 | 5 -- 25 | 5 | 1 -- 5 | | 1 -- 1

LCM of 30,50 = 2 × 3 × 5 × 5 = 150

 5. Prime Numbers Prime numbers Gives you prime calculation in the following situation. 1. Check given number (45) is prime or not 45 is not prime. Because 45 is divisible by 3 2. Find the Nth (5th) prime number Prime : 5 = 11 3. Find all prime numbers between given two numbers (1 and 10) Prime:1 = 2 Prime:2 = 3 Prime:3 = 5 Prime:4 = 7 4. Find all prime numbers between Mth prime & Nth prime (1st prime and 10th prime) Prime:1 = 2 Prime:2 = 3 Prime:3 = 5 Prime:4 = 7 Prime:5 = 11 Prime:6 = 13 Prime:7 = 17 Prime:8 = 19 Prime:9 = 23 Prime:10 = 29
 6. Simplifying Numerical Expressions Simplifying Numerical Expressions : Gives you number crunching for a given numerical expressions with or without variables. Eg: Inupt Expression without variables is 4^2+(4+2)/2 * (5+2) = 4 2 + ( 4 + 2 ) / 2 × ( 5 + 2 ) = 16 + ( 4 + 2 ) / 2 × ( 5 + 2 ) = 16 + 6 / 2 × ( 5 + 2 ) = 16 + 3 × ( 5 + 2 ) = 16 + 3 × 7 = 16 + 21 = 37 Inupt Expression with variables is 4^2+(4+2)/2* (5+2)+s and Given Values are s=1 = 4 2 + ( 4 + 2 ) / 2 × ( 5 + 2 ) + 1 = 16 + ( 4 + 2 ) / 2 × ( 5 + 2 ) + 1 = 16 + 6 / 2 × ( 5 + 2 ) + 1 = 16 + 3 × ( 5 + 2 ) + 1 = 16 + 3 × 7 + 1 = 16 + 21 + 1 = 37 + 1 = 38

 7.1 Decimal To Fraction 1. For Non-Recurring number like 6.5 = 65/10 = (13 * 5)/(2 * 5) = 13/2 2. For Recurring number like 3.142857142857 = 22/7

 7.3 Round off decimal number Round off decimal number at several places like Round Off 744.636 at ten - 10 place = 740 744.636 at zero - 0 place = 745 744.636 at tenth - .01 place = 745.64
 8. Scientific Notation Conversion 1. Decimal number To Scientific notation like 0.008 = 8 × 10-3 2. Scientific notation To Decimal number like 8 × 10-3 = 0.008

 1.1 Reduced Terms Find Reduced terms of 63/6 (63)/(6) = (3 * 3 * 7)/(2 * 3) = (21)/(2)
 1.2 Improper Fraction To Mixed Number Find Improper Fraction to Mixed Number of 63/6 (63)/(6) = (3 * 3 * 7)/(2 * 3) = (21)/(2) = 10(1)/(2)

1.3 Fraction to decimal
Find Fraction to decimal of 1/8

(1)/(8) = (1 * 125)/(8 * 125) = (125)/(1000) = 0.125

 0 0.1 2 5

8|
 1 0 0 0

|
 8

|

|
 2 0

|
 1 6

|

|
 4 0

|
 4 0

|

|
 0

 2. Mixed Number To Improper Fraction Find Mixed Number to Improper Fraction of 3 4/5 3(4)/(5) = ((3 * 5) + 4)/(5) = (19)/(5)

 3. Compare Two Fraction Compare Two Fractions 3/4 and 4/5 Here, LCM of 4 and 5 = 20 3/4 = 15/20 4/5 = 16/20 15/20 < 16/20 so 3/4 < 4/5
 4. Addition, Subtraction, Multiplication and Division of Fraction Numbers Find 3/4 + 4/5 + 5/6 =(3)/(4)+(4)/(5)+(5)/(6) =(3 * 15 + 4 * 12 + 5 * 10)/(60) =(45 + 48 + 50)/(60) =(143)/(60)

10. Power: Square, Cube, Nth Power
1. Find Square of 100

 2 | 100 2 | 50 5 | 25 5 | 5 | 1

100
= 22 × 52
= 24 × 54 (Taking square)
= 10000
11. Radical: Square root, Cube root, Nth Root
1. Find Square Root of 100

 2 | 100 2 | 50 5 | 25 5 | 5 | 1

100
= 22 × 52
= 2 × 5 (Taking square root)
= 10

12. Square Root with all steps
1. Find Square Root of 4096 with all steps

 6 | 40 96 | 64 6 | 36 | 124 | 496 4 | 496 | 128 | 0 Dividend = 40 96 Square Root = 64 Remainder = 0
13. Cube Root with all steps
2. Find Cube Root of 4096 with all steps

 1 | 4 096 | 16 1 | 1 | 516 | 3096 516 = 300(=1^2 * 300) + 180(=1*30*6) + 36(=6^2) 6 | 3096 | --- | 0 Dividend = 4 096 CubeRoot = 16 Remainder = 0

12.1 Find the smallest no which must be Added from / to 180 to make it perfect Square

 1 | 1 80 | 13 1 | 1 | 23 | 80 3 | 69 | 26 | 11 Dividend = 1 80 Square Root = 13 Remainder = 11

∴ Number to be Added = 16
∴ Perfect Square No = 180 + 16 = 196
∴ √196 = 14
12.2 Find Least number of 4 digits which is a perfect Square

Least number of 4 digits = 1000
 3 | 10 00 | 31 3 | 9 | 61 | 100 1 | 61 | 62 | 39 Dividend = 10 00 Square Root = 31 Remainder = 39

∴ Number to be Added = 24
∴ Perfect Square No = 1000 + 24 = 1024
∴ √1024 = 32

 14. Divisibility test by 2,4,8, 3,9, 5,10, 7,11,13,17,19,23,29,31,37,41,43 The given number is divisble by 2,4,8,16, 3,9, 5,10, 7,11,13,17,19,23,29 using divisibility rules 1. Check whether 100 is divisible by 2 or not?Last Digit of 100 is 0, which is divisible by 2 So 100 is divisible by 2 2. Check whether 123 is divisible by 3 or not? Sum of Digits of 123 is 6, which is divisible by 3 So 123 is divisible by 3
15. Number Series
Find the next term in the number series
1. 3, 6, 18, 72, ___
2. 1, 3, 5, 7, 9, ___
3. 2, 4, 8, 16, 32, ___
4. 20, 19, 17, 14, 10, ___
5. 1, 4, 9, 16, 25, ___
6. 1, 9, 17, 33, 49, 73, ___
7. 10, 28, 82, 244, ___

1. Find Next Number in Series 3,6,18,72,?

The given series is 3,6,18,72,?

 3 6 18 72 360 xx2 xx3 xx4 xx5

:. The next number for given series 3,6,18,72 is 360

2. Find Next Number in Series 1,4,9,16,25,?

The given series is 1,4,9,16,25,?

Series are based on square of a number 1=1^2,4=2^2,9=3^2,16=4^2,25=5^2

:. The next number for given series 1,2,3,4,5 is 6

:. Next possible number is 6^2=36

:. The next number for given series 1,4,9,16,25 is 36

3. Find Next Number in Series 1,9,17,33,49,73,?

The given series is 1,9,17,33,49,73,?

 1 9 17 33 49 73 97 +8 +8 +16 +16 +24 +24

:. The next number for given series 1,9,17,33,49,73 is 97

 1. Angle to Degree, Minute & Second Conversion 1. Convert Angle 117.748 to Degree Minute & Second 117.748^circ = 117^circ + 0.748^circ = 117^circ + 0.748 xx (60')/(1 ^circ) = 117^circ + 44.88' = 117^circ + 44' + 0.88' = 117^circ + 44' + 0.88 xx (60text{"})/(1') = 117^circ + 44' + 52.8text{"} = 117^circ 44' 53text{"}
2. Degree Conversion

 "15 Radian"  xx ("180") / ("π") " = "  "15 Radian"  xx ("180") / ("3.1416") " = "  "859.4367 Degree"

 Degree = 859.437 Radian = 15

3. Length Conversion
kilometer to meter
meter to decimeter, centimeter
centimeter to millimeter
mile to yard
yard to feet, inches
foot to inches
inch to centimeters
any to any

1. Convert 10 Kilometers to Meters

 "10 kilometers"  xx ("1000 meters") / ("1 kilometer") " = "  "10000 meters"

 Kilometers = 10 Meters = 10000 Decimeters = 100000 Centimeters = 1e+06 Millimeters = 1e+07 Miles = 6.2137 Yards = 10936.1 Feet = 32808.4 Inches = 393701
4. Mass Conversion
kilogram to gram
gram to milligram,kilogram
metric ton to kilograms
metric ton to short ton
pound to ounces
kilogram to pounds
any to any

1. Convert 15 Kilograms to Grams

 "15 kilograms"  xx ("1000 grams") / ("1 kilogram") " = "  "15000 grams"

 Kilograms = 15 Grams = 15000 Milligrams = 1.5e+07 Metric tons = 0.015 Short Tons = 0.0165 Pounds = 33.069 Ounces = 529.104

5. Money Conversion
Rupee to Paise
Paise to Rupee

1. Convert 5 Rupees to Paise

 "5 Rupees"  xx ("100 Paise") / ("1 Rupees") " = "  "500 Paise"

 Rupees = 5 Paise = 500
6. Power Conversion
hoursepower to watt
watt to joule/second
hoursepower to joule/second
any to any

1. Convert 19 Horsepower to Watts

 "19 horsepower"  xx ("746 watts") / ("1 horsepower") " = "  "14174 watts"

 Horsepower = 19 Watt = 14174

7. Speed Conversion
Kilometer/Hour to Meter/Second
Kilometer/Hour to Mile/Second
Meter/Second to Kilometer/Hour
Miles/Hour to Feet/Hour
any to any

1. Convert 10 Kilometers/Hour to Meters/Second

("10 kilometer") / ("1 hour") xx ("1000 meters") / ("1 kilometer") xx ("1 hour") / ("3600 second") " = "  "2.7778 meters/second"

 kilometers/hour = 10 meters/hour = 10000 meters/second = 2.7778 miles/hour = 6.2137 feet/hour = 32808.3
8. Temperature Conversion
Celcius to Fernhit
Celcius to Kelvin
Fernhit to Celcius
any to any

1. Convert 19 C to F

We have Given, C = 19

We know that, F = (1.8 × C) + 32
Þ F = (1.8 × 19) + 32
Þ F = 34.2 + 32
Þ F = 66.2

 Celcius = 19 Kelvin = 292.15 Fernhit = 66.2

9. Time Conversion
Year to Month
Month to Week
Week to Day
Day to Hour
Hour to Minute
Minute to Second
any to any

1. Convert 30 Days to Minutes

 "30 days"  xx ("24 hours") / ("1 day") xx ("60 minutes") / ("1 hour") " = "  "43200 minutes"

 Years = 0.0822 Months = 1 Weeks = 4.2857 Days = 30 Hours = 720 Minutes = 43200 Seconds = 2.592e+06
10. Volume Conversion
quart to pint
pint to cup
cup to fluid ounce
fluid ounce to cubic inch
cubic inches to cubic foot
cubic foot to liters
liter to milliliter, cubic decimeter
milliliter to cubic centimeter
any to any

1. Convert 17 Liters to Milliliters

 "17 liters"  xx ("1000 milliliters") / ("1 liter") " = "  "17000 milliliters"

 Gallons = 4.4904 Quarts = 17.9617 Pints = 35.9234 Cups = 71.8468 Fluid Ounces = 574.774 Cubic Inches = 1037.29 Cubic Feet = 0.6003 Liters = 17 Milliliters = 17000

1. Decimal to Binary
(42)10 = (__________)2
(124)10 = (__________)2
(24.625)10 = (__________)2
(42.5)10 = (__________)2

1. Convert Decimal number (42)10 to Binary number

(42)10 = (_______)2

 2 | 42 | 2 | 21 | 0 ↑ ↑ 2 | 10 | 1 ↑ ↑ 2 | 5 | 0 ↑ ↑ 2 | 2 | 1 ↑ ↑ 2 | 1 | 0 ↑ ↑ | 0 | 1 ↑

∴ (42)10 = (101010)2
2. Decimal to Octal
(425)10 = (__________)8
(1425)10 = (__________)8
(3445.865)10 = (__________)8
(1423.25)10 = (__________)8

1. Convert Decimal number (425)10 to Octal number

(425)10 = (_______)8

 8 | 425 | 8 | 53 | 1 ↑ ↑ 8 | 6 | 5 ↑ ↑ | 0 | 6 ↑

∴ (425)10 = (651)8

(1423)10 = (__________)16
(42356)10 = (__________)16
(4234.456)10 = (__________)16
(1234.567)10 = (__________)16

1. Convert Decimal number (1423)10 to Hexadecimal number

(1423)10 = (_______)16

 16 | 1423 | 16 | 88 | F ↑ ↑ 16 | 5 | 8 ↑ ↑ | 0 | 5 ↑

∴ (1423)10 = (58F)16
 4. Binary to Decimal (110101001)2 = (__________)10 (1100111101)2 = (__________)10 (11010.1001)2 = (__________)10 (11011.101001)2 = (__________)10 1. Convert Binary number (110101001)2 to Decimal number (110101001)2 = (_______)10110101001 = 1 × 20 + 0 × 21 + 0 × 22 + 1 × 23 + 0 × 24 + 1 × 25 + 0 × 26 + 1 × 27 + 1 × 28 = 1 × 1 + 0 × 2 + 0 × 4 + 1 × 8 + 0 × 16 + 1 × 32 + 0 × 64 + 1 × 128 + 1 × 256 = 425∴ (110101001)2 = (425)10

5. Binary to Octal
(11011001)2 = (__________)8
(11110101001)2 = (__________)8
(111101.01001)2 = (__________)8
(11110101.00101001)2 = (__________)8

1. Convert Binary number (11011001)2 to Octal number

(11011001)2 = (_______)8

 011 011 001 3 3 1

∴ (11011001)2 = (331)8
(11110101001)2 = (__________)16
(111101010110001)2 = (__________)16
(11110.101001)2 = (__________)16
(111101.1101001)2 = (__________)16

1. Convert Binary number (11110101001)2 to Hexadecimal number

(11110101001)2 = (_______)16

 0111 1010 1001 7 A 9

∴ (11110101001)2 = (7A9)16

 7. Octal to Decimal (1534)8 = (__________)10 (12340)8 = (__________)10 (127.32)8 = (__________)10 (432.12)8 = (__________)10 1. Convert Octal number (1534)8 to Decimal number (1534)8 = (_______)101534 = 4 × 80 + 3 × 81 + 5 × 82 + 1 × 83 = 4 × 1 + 3 × 8 + 5 × 64 + 1 × 512 = 860∴ (1534)8 = (860)10
8. Octal to Binary
(4321)8 = (__________)2
(12345)8 = (__________)2
(123.45)8 = (__________)2
(7012.053)8 = (__________)2

1. Convert Octal number (4321)8 to Binary number

(4321)8 = (_______)2

 4 3 2 1 100 011 010 001

∴ (4321)8 = (100011010001)2

(7012)8 = (__________)16
(123456)8 = (__________)16
(1234.567)8 = (__________)16
(703.056)8 = (__________)16

1. Convert Octal number (7012)8 to Hexadecimal number

First Method
(7012)8 = (_______)16

First Converting Base-8 to Base-2(7012)8 = (_______)2

 7 0 1 2 111 000 001 010

∴ (7012)8 = (111000001010)2

Now Converting Base-2 to Base-16(111000001010)2 = (_______)16

 1110 0000 1010 E 0 A

∴ (111000001010)2 = (E0A)16

∴ (7012)8 = (E0A)16

OR

Second Method
(7012)8 = (_______)16

First Converting Base-8 to Base-10(7012)8 = (_______)10

7012
= 2 × 80 + 1 × 81 + 0 × 82 + 7 × 83
= 2 × 1 + 1 × 8 + 0 × 64 + 7 × 512
= 3594
∴ (7012)8 = (3594)10

Now Converting Base-10 to Base-16(3594)10 = (_______)16

 16 | 3594 | 16 | 224 | A ↑ ↑ 16 | 14 | 0 ↑ ↑ | 0 | E ↑

∴ (3594)10 = (E0A)16
∴ (7012)8 = (E0A)16
(FC3A)16 = (__________)8
(ABCD)16 = (__________)8
(1524.03)16 = (__________)8
(BCF.52A)16 = (__________)8

1. Convert Hexadecimal number (FC3A)16 to Octal number

First Method
(FC3A)16 = (_______)8

First Converting Base-16 to Base-2(FC3A)16 = (_______)2

 F C 3 A 1111 1100 0011 1010

∴ (FC3A)16 = (1111110000111010)2

Now Converting Base-2 to Base-8(1111110000111010)2 = (_______)8

 001 111 110 000 111 010 1 7 6 0 7 2

∴ (1111110000111010)2 = (176072)8

∴ (FC3A)16 = (176072)8

OR

Second Method
(FC3A)16 = (_______)8

First Converting Base-16 to Base-10(FC3A)16 = (_______)10

FC3A
= A × 160 + 3 × 161 + C × 162 + F × 163
= 10 × 1 + 3 × 16 + 12 × 256 + 15 × 4096
= 64570
∴ (FC3A)16 = (64570)10

Now Converting Base-10 to Base-8(64570)10 = (_______)8

 8 | 64570 | 8 | 8071 | 2 ↑ ↑ 8 | 1008 | 7 ↑ ↑ 8 | 126 | 0 ↑ ↑ 8 | 15 | 6 ↑ ↑ 8 | 1 | 7 ↑ ↑ | 0 | 1 ↑

∴ (64570)10 = (176072)8
∴ (FC3A)16 = (176072)8

(A35)16 = (__________)2
(BBA)16 = (__________)2
(1085.FB)16 = (__________)2
(FC.3A)16 = (__________)2

1. Convert Hexadecimal number (A35)16 to Binary number

(A35)16 = (_______)2

 A 3 5 1010 0011 0101

∴ (A35)16 = (101000110101)2
 12. Hexadecimal to Decimal (7036)16 = (__________)10 (BCA)16 = (__________)10 (A0.36)16 = (__________)10 (100.35)16 = (__________)10 1. Convert Hexadecimal number (BCA)16 to Decimal number (BCA)16 = (_______)10BCA = A × 160 + C × 161 + B × 162 = 10 × 1 + 12 × 16 + 11 × 256 = 3018∴ (BCA)16 = (3018)10

13. Decimal to Other
(1536)10 = (__________)6
(5336)10 = (__________)14
(45236)10 = (__________)24
(155436)10 = (__________)18

1. Convert Decimal number (383)10 to Base 20 number (_______)20

(383)10 = (_______)20

 20 | 383 | 20 | 19 | 3 ↑ ↑ | 0 | J ↑

∴ (383)10 = (J3)20
 14. Other to Decimal (155436)7 = (__________)10 (BA012)12 = (__________)10 (GH012)20 = (__________)10 (1230321)4 = (__________)10 1. Convert (64X)35 means 64X where its Base 35 to Decimal number (64X)35 = (_______)1064X = X × 350 + 4 × 351 + 6 × 352 = 33 × 1 + 4 × 35 + 6 × 1225 = 7523∴ (64X)35 = (7523)10

 18. Tables 1. Addition table 2. Addition table 1 to 30 3. Multiplication table rows and columns 4. Multiplication table 1 to 30 5. Roman table 6. Square table 7. Cube table 8. Power table 9. Factorial table 10. Sin table 11. Cos table 12. Tan table 13. Sin, Cos, Tan table 14. Area formula 15. Volume formula 16. Factors formula 17. Number system conversion table

 6. Place Value Place Value Gives you the place value of a number in the given number Eg:- Given number = 123456 and find place value = 3 then We have given no is 123456 and the place value of 3 is (3 × 1000) = 3000
 7. Leap Year Leap Year Gives you the given year is leap year or not. Eg:- Year = 2005 is not a leap year. Also gives the list of leap years between given two years. Eg:- Find Leap Years between the years 1955 and 2055 Leap Years Between the years 1955 and 2055 are given below... 1 : 1956 2 : 1960 3 : 1964 4 : 1968 5 : 1972 6 : 1976 7 : 1980 8 : 1984 9 : 1988 10 : 1992 11 : 1996 12 : 2000 13 : 2004 14 : 2008 15 : 2012 16 : 2016 17 : 2020 18 : 2024 19 : 2028 20 : 2032 21 : 2036 22 : 2040 23 : 2044 24 : 2048 25 : 2052


text:
Huggingface’s datasets package has proven to be 🔥 for sharing NLP datasets. So, why aren’t people sharing other types of datasets? Well, frankly, idk. So, lets be the change we wanna see in the world and start sharing our own 😎.

This post will walk you through sharing an image classification dataset on HuggingFace’s Dataset Hub with minimal friction. We’ll use Microsoft’s Cats and Dogs dataset as an example.

When we're done, you'll be able to do something like this

## Data Preparation

curl -O https://download.microsoft.com/download/3/E/1/3E1C3F21-ECDB-4869-8368-6DEBA77B919F/kagglecatsanddogs_3367a.zip
unzip -q kagglecatsanddogs_3367a.zip

This will create a new directory called PetImages, which has two folders - Cat/ and Dog/ within it. These directories both hold images corresponding to their names.

### Fixing Corrupted Images

Unfortunately, the dataset comes with corrupted images. These will really screw you up when training, so we’ll need to get rid of them. To do so, we’ll use a script modified from keras’s documentation. You’ll need to pip install tensorflow-cpu (or any version of tensorflow) to run it.

from pathlib import Path

import tensorflow as tf

root = Path('./PetImages')
num_skipped = 0
for folder_name in ("Cat", "Dog"):

# Loop over all image files
for fpath in (root / folder_name).glob('*'):
with fpath.open('rb') as f:
# If is_jfif, the image is fine. If not, we delete it.
is_jfif = tf.compat.as_bytes('JFIF') in f.peek(10)
if is_jfif:
continue

num_skipped+=1

# Deletes the file

print("Deleted %d images" % num_skipped)

### Exporting to Bytes

We can actually take that script above and modify it slightly to export our images as bytes. Representing the images as bytes instead of files makes them play nice with pyarrow, and subsequently Huggingface’s datasets package.

Here, we basically do the same thing, except when we come across valid images, we store them in a list of dicts called examples. Each dict will look like:

{'img_bytes': <the bytes>, 'labels': <the string label>}

After gathering all the examples, we simply save them as a pickle file, train.pt. This will be the source data we upload to the hub.

import pickle
from pathlib import Path

import tensorflow as tf

root = Path('./PetImages')
num_skipped = 0
examples = []
for folder_name in ("Cat", "Dog"):
for fpath in (root / folder_name).glob('*'):
with fpath.open('rb') as f:
is_jfif = tf.compat.as_bytes('JFIF') in f.peek(10)
if is_jfif:
continue

num_skipped+=1

print("Deleted %d images" % num_skipped)

with Path("train.pt").open('wb') as f:
pickle.dump(examples, f)

🤓 The preparation script can also be found in in my repo

## Sharing our Dataset

Now, we can share our dataset with the world! Let’s see how that works…

First, create a repo on HuggingFace’s hub. You’ll need an account to do so, so go sign up if you haven’t already! Also, you’ll need git-lfs , which can be installed from here.

huggingface-cli repo create cats-and-dogs --type dataset

Then, cd into that repo and make sure git lfs is enabled.

cd cats-and-dogs/
git lfs install

After that, we can copy our train.pt file into that repo.

🚨 Make sure to git lfs track your source file before adding/commiting it 🚨

cp ../train.pt .
git lfs track *.pt

Finally, we can add, commit, and push the .gitattributes file that stores tracking info + the train.pt file we created earlier. We’ll push the data before writing the required loader script, as its easier to develop that way.

git add .gitattributes
git commit -m "initial commit"
git push -u origin main

### Writing the Builder Script

Now we can write the script that’ll be used to load our dataset. If you’re impatient, it can be found here. It’s easy! We’ll first create a file called cats-and-dogs.py in our repo. Then, we’ll set up some imports and global variables that will be used later. They’re pretty self explanatory, so I’ll just let you read them.

import pickle
from pathlib import Path
from typing import List

import datasets

_URL = "https://huggingface.co/datasets/nateraw/cats-and-dogs/resolve/main/"
_URLS = {
"train": _URL + "train.pt",
}
_DESCRIPTION = "A large set of images of cats and dogs. There are 1738 corrupted images that are dropped."
_CITATION = """\
author = {Elson, Jeremy and Douceur, John (JD) and Howell, Jon and Saul, Jared},
title = {Asirra: A CAPTCHA that Exploits Interest-Aligned Manual Image Categorization},
booktitle = {Proceedings of 14th ACM Conference on Computer and Communications Security (CCS)},
year = {2007},
month = {October},
publisher = {Association for Computing Machinery, Inc.},
edition = {Proceedings of 14th ACM Conference on Computer and Communications Security (CCS)},
}
"""

Now, we’ll create the class itself, inheriting from datasets.GeneratorBasedBuilder. We only need to define 3 simple functions.

#### 1. _info

This sets up some metadata and the schema of your data. The key here for our dataset is that we use datasets.Value('binary') to represent the byte data we created before.

Also, notice that the names of the features are the same as what we exported before. This will make things easier later.

class CatsAndDogs(datasets.GeneratorBasedBuilder):
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"img_bytes": datasets.Value("binary"),
"labels": datasets.features.ClassLabel(names=["cat", "dog"]),
}
),
supervised_keys=("img_bytes", "labels"),
homepage=_HOMEPAGE,
citation=_CITATION,
)

#### 2. _split_generators

This function is used to define steps for downloading + preparing dataset splits. We won’t be splitting the dataset, as there aren’t official splits distributed by Microsoft.

class CatsAndDogs(datasets.GeneratorBasedBuilder):

# ...

return [
]

Basically, the dl_manager will take the dict from _URLS and download the file we asked for. It returns downloaded_files, which is a dict mapping _URLS’s keys to the actual filepath its values were downloaded to.

We pass gen_kwargs={'filepath': downloaded_files['train']} to the SplitGenerator to specify the signature of the next function - _generate_examples. Whatever you define in the dict here will be what is passed to that function.

#### 3. _generate_examples

We define _generate_examples with a signature matching the dict we defined in _split_generators. We’ll get the filepath of our train.pt file, which we can then unpickle before generating examples. Since we used the same keys in examples’s inner dicts as the features we defined in _info, we can simply return each item. We also include a string identifier by using the iteration’s indices.

class CatsAndDogs(datasets.GeneratorBasedBuilder):

# ...

def _generate_examples(self, filepath):
"""This function returns the examples in the raw (text) form."""
logger.info("generating examples from = %s", filepath)

with Path(filepath).open("rb") as f:

for i, ex in enumerate(examples):
yield str(i), ex

### Running it Locally

We can test that our file works by running it locally before uploading it to our repo. To do that, we just pass the name of the file, cats-and-dogs.py, instead of a HF Hub identifier. This will download and load the files we already uploaded.

from datasets import load_dataset

Once you see that work successfully, we can push it up to the repo:

git add cats-and-dogs.py
git push

### Using it in PyTorch

#### Converting to PIL from Bytes

Now that our dataset is public, we can load it using datasets.load_dataset, passing it '<your-username>/cats-and-dogs'.

from datasets import load_dataset

If you take a look at ds['train'][0], you’ll notice we still have image bytes, not a real image. To fix that, we simply convert the images back to PIL.Image. We’ll use ds.with_transform instead of map, because its faster in real time. It might be more efficient to use map instead, though (I haven’t tested it 😅)

from io import BytesIO
from PIL import Image

import datasets

def bytes_to_pil(example_batch):
example_batch['img'] = [
Image.open(BytesIO(b)) for b in example_batch.pop('img_bytes')
]
return example_batch

ds = ds.with_transform(bytes_to_pil)

Now, when we look at an example, we see we have a friendly PIL.Image type instead of bytes:

{'img': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=500x448 at 0x7FACF1411F50>,
'labels': 0}

#### Usage Example

Here’s a more complete example showing how to convert your data to tensors and then to a torch.utils.data.DataLoader.

from io import BytesIO
from PIL import Image
from torchvision.transforms import (
Compose,
ToTensor,
RandomResizedCrop,
RandomHorizontalFlip,
Normalize,
Resize,
CenterCrop
)
import datasets

_train_transforms = Compose([
RandomResizedCrop(224),
RandomHorizontalFlip(),
ToTensor(),
Normalize([0.500, 0.500, 0.500], [0.500, 0.500, 0.500])
])

def bytes_to_pil(b):
return Image.open(BytesIO(b))

def apply_train_transforms(example_batch):
example_batch['pixel_values'] = [
_train_transforms(bytes_to_pil(b)) for b in example_batch.pop('img_bytes')
]
return example_batch

data['train'].with_transform(apply_train_transforms),
batch_size=32,
num_workers=2,
)

We can take a look at a single batch to make sure it works:

batch = next(iter(train_loader))
print(batch['pixel_values'].shape, batch['labels'].shape)
# >>> (torch.Size([32, 3, 224, 224]), torch.Size([32]))

Now you’ve got a dataloader ready to train any image classification model in PyTorch! 🚀

for batch in train_loader:
out = your_model(**batch)
# ...

## Conclusion

This post taught you how to use HuggingFace’s datasets` package to upload image classification datasets to the HuggingFace Hub. This same strategy can be used to upload video, audio, segmentation masks, etc.

If this tutorial helped you out, feel free to follow me on twitter or github to stay up to date on my next tutorials. If you give this a try, have any questions, or have a suggestion for future tutorials you’d like to see, shoot me a message on Twitter.

Cheers 🍻

format: plain


text:
# Netzschleuder network catalogue, repository and centrifuge

Problems with this dataset? Open an issue.
You may also take a look at the source code.
The network in this dataset can be loaded directly from graph-tool with:
import graph_tool.all as gt
g = gt.collection.ns["game_thrones"]

# game_thrones — Game of Thrones coappearances

Description

Network of coappearances of characters in the Game of Thrones series, by George R. R. Martin, and in particular coappearances in the book "A Storm of Swords." Nodes are unique characters, and edges are weighted by the number of times the two characters' names appeared within 15 words of each other in the text.1

1. Description obtained from the ICON project.

Tags
Social Fictional Weighted
Citation
Upstream URL OK
http://www.macalester.edu/~abeverid/data/stormofswords.csv
Networks
Tip: hover your mouse over a table header to obtain a legend.
Name Nodes Edges $\left<k\right>$ $\sigma_k$ $\lambda_h$ $\tau$ $r$ $c$ $\oslash$ $S$ Kind Mode NPs EPs gt GraphML GML csv
game_thrones 107 352 6.58 6.58 11.74 14.10 -0.13 0.33 6 1.00 Undirected Unipartite name weight 3 KiB 5 KiB 4 KiB 3 KiB
Ridiculograms


text:
Welcome to ZOJ
 Contests Information Problems Runs Statistics Ranklist Clarification
149 - The 2017 China Collegiate Programming Contest, Qinhuangdao Site - J
Tree Equation

Time Limit: 3 Seconds      Memory Limit: 131072 KB      Special Judge

Tired of solving mathematical equations, DreamGrid starts to solve equations related to rooted trees.

Let $$A$$ and $$B$$ be two two arbitrary rooted trees and $$r(T)$$ denotes the root of $$T$$. DreamGrid has defined two basic operations:

• Addition. $$T = A + B$$ is built by merging the two roots $$r(A)$$, $$r(B)$$ into a new root $$r(T)$$. That is the subtrees of $$A$$ and $$B$$ (if any) become the subtrees of $$r(T)$$.
• Multiplication. $$T = A \cdot B$$ is built by merging $$r(B)$$ with each vertex $$x \in A$$ so that all the subtrees of $$r(B)$$ become new subtrees of $$x$$.

Given three rooted trees $$A$$, $$B$$ and $$C$$, DreamGrid would like to find two rooted trees $$X$$ and $$Y$$ such that $$A \cdot X + B \cdot Y = C$$.

Input

There are multiple test cases. The first line of input contains an integer $$T$$, indicating the number of test cases. For each test case:

The first line contains three integers $$n_a$$, $$n_b$$ and $$n_c$$ ($$2 \le n_a, n_b \le n_c \le 10^5$$) -- the number of vertices in rooted tree $$A$$, $$B$$ and $$C$$, respectively.

The second line contains $$n_a$$ integers $$a_1, a_2, \dots, a_{n_a}$$ ($$0 \le a_i < i$$) -- where $$a_i$$ is the parent of the $$i$$-th vertex in tree $$A$$.

The third line contains $$n_b$$ integers $$b_1, b_2, \dots, b_{n_b}$$ ($$0 \le b_i < i$$) -- where $$b_i$$ is the parent of the $$i$$-th vertex in tree $$B$$.

The fourth line contains $$n_c$$ integers $$c_1, c_2, \dots, c_{n_c}$$ ($$0 \le c_i < i$$) -- where $$c_i$$ is the parent of the $$i$$-th vertex in tree $$C$$.

Note that if $$a_i=0$$ ($$b_i=0$$ or $$c_i=0$$), then the $$i$$-th vertex is the root of the tree $$A$$ ($$B$$ or $$C$$).

It is guaranteed that the sum of all $$n_c$$ does not exceed $$2 \times 10^6$$.

Output

For each test case, if you can not find a solution, output "Impossible" (without quotes) in the first line.

Otherwise, output two integers $$n_x$$ and $$n_y$$ ($$1 \le n_x, n_y \le 10^5$$) denoting the number of vertices in rooted tree $$X$$ and $$Y$$ in the first line.

Then in the second line, output $$n_x$$ integers $$x_1, x_2, \dots, x_{n_x}$$ ($$0 \le x_i < i$$) -- where $$x_i$$ is the parent of the $$i$$-th vertex in tree $$X$$.

Then in the third line, output $$n_y$$ integers $$y_1, y_2, \dots, y_{n_y}$$ ($$0 \le y_i < i$$) -- where $$y_i$$ is the parent of the $$i$$-th vertex in tree $$Y$$.

If there are multiple solutions, print any of them.

Sample Input

2
2 3 10
0 1
0 1 2
0 1 1 3 4 3 6 3 1 9
4 3 10
0 1 2 2
0 1 2
0 1 1 3 4 3 6 3 1 9

Sample Output

Impossible
2 1
0 1
0

Submit    Status


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For this example we’ll build a linear discriminant analysis model to classify which species a given flower belongs to. fisher's linear discriminant in Python. Linear discriminant analysis (LDA) is used here to reduce the number of features to a more manageable number before the process of classification. Most of the text book covers this topic in general, however in this Linear Discriminant Analysis – from Theory to Code tutorial we will understand both the mathematical derivations, as well how to implement as simple LDA using Python code. As the name implies dimensionality reduction techniques reduce the number of dimensions (i.e. First, we’ll load the necessary functions and libraries for this example: For this example, we’ll use the iris dataset from the sklearn library. Linear Discriminant Analysis in Python (Step-by-Step) Published by Zach. In this post, we’ll review a family of fundamental classification algorithms: linear and quadratic discriminant analysis. Let’s see how we could go about implementing Linear Discriminant Analysis from scratch using Python. The algorithm entails creating a probabilistic mannequin per class primarily based on the precise distribution of observations for every enter variable. Thus, we encode every class as a number so that we can incorporate the class labels into our plot. Linear and Quadratic Discriminant Analysis¶ Exploring the theory and implementation behind two well known generative classification algorithms: Linear discriminative analysis (LDA) and Quadratic … For binary classification, we can find an optimal threshold t and classify the data accordingly. Linear Discriminant Analysis. This category of dimensionality reduction techniques are used in biometrics [12,36], Bioinfor-matics [77], and chemistry [11]. If we’d like to reduce the number of dimensions down to 1, one approach would be to project everything on to the x-axis. 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Take a look, X = pd.DataFrame(wine.data, columns=wine.feature_names), class_feature_means = pd.DataFrame(columns=wine.target_names), within_class_scatter_matrix = np.zeros((13,13)), between_class_scatter_matrix = np.zeros((13,13)), eigen_values, eigen_vectors = np.linalg.eig(np.linalg.inv(within_class_scatter_matrix).dot(between_class_scatter_matrix)), pairs = [(np.abs(eigen_values[i]), eigen_vectors[:,i]) for i in range(len(eigen_values))], pairs = sorted(pairs, key=lambda x: x[0], reverse=True), w_matrix = np.hstack((pairs[0][1].reshape(13,1), pairs[1][1].reshape(13,1))).real, from sklearn.discriminant_analysis import LinearDiscriminantAnalysis, X_train, X_test, y_train, y_test = train_test_split(X_lda, y, random_state=1), 10 Statistical Concepts You Should Know For Data Science Interviews, 7 Most Recommended Skills to Learn in 2021 to be a Data Scientist. Then, we plug the mean vectors (mi) into the equation from before in order to obtain the within class scatter matrix. 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In our previous article Implementing PCA in Python with Scikit-Learn, we studied how we can reduce dimensionality of the feature set using PCA.In this article we will study another very important dimensionality reduction technique: linear discriminant analysis (or LDA). Thus, we sort the eigenvalues from highest to lowest and select the first k eigenvectors. Make learning your daily ritual. Linear Discriminant Analysis, or LDA, is a linear machine learning algorithm used for multi-class classification.. Lastly, we can create an LDA plot to view the linear discriminants of the model and visualize how well it separated the three different species in our dataset: You can find the complete Python code used in this tutorial here. Linear Discriminant Analysis finds the area that maximizes the separation between multiple classes. 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Multi-class Linear Discriminant Analysis; Edit on GitHub; Multi-class Linear Discriminant Analysis ¶ Multi-class LDA is a generalization of standard two-class LDA that can handle arbitrary number of classes. where c is the total number of distinct classes and. Name * Email * Website. Get the spreadsheets here: Try out our free online statistics calculators if you’re looking for some help finding probabilities, p-values, critical values, sample sizes, expected values, summary statistics, or correlation coefficients. The dataset contains 178 rows of 13 columns each. Linear Discriminant Analysis (LDA) is an important tool in both Classification and Dimensionality Reduction technique. Specifically, the model seeks to find a linear combination of input variables that achieves the maximum separation for samples between classes (class centroids or means) and the minimum separation of samples within each class. Visualize the Results of LDA Model. 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We can also use the model to predict which class a new flower belongs to, based on input values: We can see that the model predicts this new observation to belong to the species called setosa. Next, let’s take a look at how LDA compares to Principal Component Analysis or PCA. Prerequisites. Data preparation Model training and evaluation Data Preparation We will be using the bioChemists dataset which comes from the pydataset module. Linear discriminant analysis is a method you can use when you have a set of predictor variables and you’d like to classify a response variable into two or more classes. In the following section we will use the prepackaged sklearn linear discriminant analysis method. 19, Oct 16. Linear discriminant analysis is supervised machine learning, the technique used to find a linear combination of features that separates two or more classes of objects or events. 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The Linear Discriminant Analysis in Python is a very simple and well-understood approach of classification in machine learning. Medical: In this field, Linear discriminant analysis … More specifically, for linear and quadratic discriminant analysis, $$P(x|y)$$ is modeled as a multivariate Gaussian distribution with density: $P(x | y=k) = \frac{1}{(2\pi)^{d/2} |\Sigma_k|^{1/2}}\exp\left(-\frac{1}{2} (x-\mu_k)^t \Sigma_k^{-1} (x-\mu_k)\right)$ View all posts by Zach Post navigation. LDA (Linear Discriminant Analysis) and QDA (Quadratic Discriminant Analysis) are expected to work well if the class conditional densities of clusters are approximately normal. To figure out what argument value to use with n_components (e.g. Just like before, we plot the two LDA components. Though there are other dimensionality reduction techniques like Logistic Regression or PCA, but LDA is preferred in many special classification cases. Thus, we express it as a percentage. by admin on April 20, 2017 with No Comments # Import the libraries import numpy as np import matplotlib.pyplot as plt import pandas as pd # Import the dataset dataset = pd.read_csv(‘LDA_Data.csv’) X = dataset.iloc[:, 0:13].values y = dataset.iloc[:, 13].values # Splitting the dataset into the Training set and Test set from … Published: March 24, 2020. Linear discriminant analysis, also known as LDA, does the separation by computing the directions (“linear discriminants”) that represent the axis that enhances the separation between multiple classes. In this post, we will learn how to use LDA with Python. Overview¶ Multi-class LDA is based on the analysis of two scatter matrices: within-class scatter matrix and between-class scatter matrix. The resulting combination may be used as a linear classifier, or, more commonly, for … LDA tries to reduce dimensions of the feature set while retaining the information that discriminates output classes. Category: Machine Learning. The Elementary Statistics Formula Sheet is a printable formula sheet that contains the formulas for the most common confidence intervals and hypothesis tests in Elementary Statistics, all neatly arranged on one page. Disciminative classifiers Linear Discriminant Analysis (LDA) in Python – Step 8.) The eigenvectors with the highest eigenvalues carry the most information about the distribution of the data. Linear Discriminant Analysis (LDA) is a dimensionality reduction technique. LDA components) by taking the dot product of the data and the matrix from step 4. Linear Discriminant Analysis with scikit learn in Python. ABOUT. #find how many total observations are in dataset, And we’ll use them to predict the response variable, Next, we’ll fit the LDA model to our data using the, scores = cross_val_score(model, X, y, scoring=', We can see that the model performed a mean accuracy of, #predict which class the new observation belongs to, We can see that the model predicts this new observation to belong to the species called, You can find the complete Python code used in this tutorial, How to Sort a Data Frame by Date in R (With Examples), Introduction to Quadratic Discriminant Analysis. Linear discriminant analysis is a classification algorithm commonly used in data science. We start off by creating and fitting an instance of the PCA class. Which makes it a supervised algorithm. The features are composed of various characteristics such as the magnesium and alcohol content of the wine. A brand new instance is then labeled … In the proceeding tutorial, we’ll be working with the wine dataset which can be obtained from the UCI machine learning repository. If you want to be an expert in machine learning, knowledge of Linear Discriminant Analysis would lead you to that … Linear Discriminant Analysis (LDA) in Python – Step 4.) If all the class share the same covariance matrix then the model is called Linear Discriminant Analysis and if each class has a different covariance matrix then the model called Quadratic Discriminant Analysis. In our previous article Implementing PCA in Python with Scikit-Learn, we studied how we can reduce dimensionality of the feature set using PCA.In this article we will study another very important dimensionality reduction technique: linear discriminant analysis (or LDA). Dimensionality reduction using Linear Discriminant Analysis¶. Learn more. In other words, Y is composed of the LDA components, or said yet another way, the new feature space. We can access the following property to obtain the variance explained by each component. Gaussian Mixture Model. Linear Discriminant Analysis (or LDA from now on), is a supervised machine learning algorithm used for classification. Linear discriminant analysis (LDA), normal discriminant analysis (NDA), or discriminant function analysis is a generalization of Fisher's linear discriminant, a method used in statistics and other fields, to find a linear combination of features that characterizes or separates two or more classes of objects or events. Linear Discriminant Analysis: LDA is used mainly for dimension reduction of a data set. How to fit, evaluate, and make predictions with the Linear Discriminant Analysis model with Scikit-Learn. row) and n is the total number of samples with a given class. Active 5 months ago. Linear Discriminant Analysis (LDA) is most commonly used as dimensionality reduction technique in the pre-processing step for pattern-classification and machine learning applications.The goal is to project a dataset onto a lower-dimensional space with good class-separability in order avoid overfitting (“curse of dimensionality”) and also reduce computational costs.Ronald A. Fisher formulated the Linear Discriminant in 1936 (The U… Hot Network Questions Samurai use of two-handed weapon The dimension of the output is necessarily less than the … I have the fisher's linear discriminant that i need to use it to reduce my examples A and B that are high dimensional matrices to simply 2D, that is exactly like LDA, each example has classes A and B, therefore if i was to have a third example they also have classes A and B, fourth, fifth and n … The jupyter notebook can be found on its github repository. Learn the linear discriminant analysis and K-Nearest Neighbors technique in Python; Preliminary analysis of data using Univariate analysis before running classification model; Predict future outcomes basis past data by implementing Machine Learning algorithm; Indepth knowledge of data collection and data preprocessing for Machine Learning logistic regression problem; Course contains … Prev How to Retrieve Row Numbers in R (With Examples) Next Linear Discriminant Analysis in R (Step-by-Step) Leave a Reply Cancel reply. As the name implies dimensionality reduction techniques reduce the number of dimensions (i.e. (ii) Linear Discriminant Analysis often outperforms PCA in a multi-class classification task when the class labels are known. variables) in a dataset while retaining as much information as possible. Required fields are marked *. Fortunately, the scitkit-learn library provides a wrapper function for downloading and. Linear Discriminant Analysis in Machine Learning with Python By Yash Gandhi Some datasets have thousands of features that give more information about data and that’s good but it takes large space and more time for computation of processing. LDA (Linear Discriminant Analysis) is a feature reduction technique and a common preprocessing step in machine learning pipelines. ML | Variational Bayesian Inference for … Linear Discriminant Analysis: LDA is used mainly for dimension reduction of a data set. Visualize the Results of LDA Model. We will learn about the concept and the math behind this popular ML algorithm, and how to implement it in Python. For every class, we create a vector with the means of each feature. The steps we will for this are as follows. It is used for modeling differences in groups i.e. Rather than implementing the Linear Discriminant Analysis algorithm from scratch every time, we can use the predefined LinearDiscriminantAnalysis class made available to us by the scikit-learn library. This tutorial provides a step-by-step example of how to perform linear discriminant analysis in Python. For that exercise, we mixed milk powder and coconut milk powder with different ratios, from 100% milk powder to 100% coconut milk powder in increments of 10%. LDA tries to reduce dimensions of the feature set while retaining the information that discriminates output classes. … So, the definition of LDA is- LDA project a feature space (N-dimensional data) onto a smaller subspace k ( k<= n-1) while maintaining the class discrimination information. Bernoulli vs Binomial Distribution: What’s the Difference. Name * … find the linear combination of … That is, we use the same dataset, split it in 70% training and 30% test data (Actually splitting the dataset is not mandatory in that case since we don't do any prediction - though, it is good practice and it would not negatively affect our results in this case - so we do it -.) All algorithms from this course can be found on GitHub together with example tests. Next, let’s see whether we can create a model to classify the using the LDA components as features. The method can be used directly without configuration, although the implementation does offer arguments for customization, such as the choice of solver and the use of a penalty. Before we dive into LDA, it’s good to get an intuitive grasp of what LDAtries to accomplish. The Linear Discriminant Analysis is available in the scikit-learn Python machine learning library via the LinearDiscriminantAnalysis class. Linear-Discriminant-Analysis click on the text below for more info. Linear discriminant analysis, also known as LDA, does the separation by computing the directions (“linear discriminants”) that represent the axis that enhances the separation between multiple classes. The linear combinations obtained using Fisher’s linear discriminant are called Fisher faces. Linear discriminant analysis (LDA), normal discriminant analysis (NDA), or discriminant function analysis is a generalization of Fisher's linear discriminant, a method used in statistics and other fields, to find a linear combination of features that characterizes or separates two or more classes of objects or events. In scikit-learn, LDA is implemented using LinearDiscriminantAnalysis includes a parameter, n_components indicating the number of features we want returned. The Linear Discriminant Analysis in Python is a very simple and well-understood approach of classification in machine learning. LDA (Linear Discriminant Analysis) is a feature reduction technique and a common preprocessing step in machine learning pipelines. For instance, suppose that we plotted the relationship between two variables where each color represent a different class. This tutorial provides a step-by-step example of how to perform linear discriminant analysis in Python. Here, we are going to unravel the black box hidden behind the … The parameters of the Gaussian distribution: ... Fisher’s Linear Discriminant, in essence, is a technique for dimensionality reduction, not a discriminant. As we can see, PCA selected the components which would result in the highest spread (retain the most information) and not necessarily the ones which maximize the separation between classes. … Most of the text book covers this topic in general, however in this Linear Discriminant Analysis – from Theory to Code tutorial we will understand both the mathematical derivations, as well how to implement as simple LDA using Python code. Python, it ’ s take linear discriminant analysis python look at how LDA compares to Principal component Analysis or,! Values, it ’ s the Difference ( i.e this example we ’ build. Provided by the second feature learn about the concept and the math behind this popular ML algorithm and. Two scatter matrices: within-class scatter matrix by taking the dot product of the new dimensions generated is a simple. On its GitHub repository Difference between the PCA and LDA algorithms by taking the dot product of the feature while... 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Second feature are known fortunately, the quadratic multiple Discriminant Analysis with Python its GitHub repository words, Y composed. A temporary array in PCA, but LDA is based on the precise distribution of observations for every enter.. Used to project the features are composed of the PCA class from before in order to obtain variance! By taking the dot product of the PCA and LDA algorithms together with example tests studying.! Classify the using the LDA technique is developed to transform the linear Discriminant Analysis seeks best.


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## GetDist: removing legend

Use of Healpix, camb, CLASS, cosmomc, compilers, etc.
Posts: 33
Joined: July 17 2014
Affiliation: Johns Hopkins University

### GetDist: removing legend

When I make plots using g = getdist.plots.getSinglePlotter then g.plots2d I seem to automatically end up with a legend across the top of my plot if I have multiple roots. Is there a way to disable this? Including something along the lines of g.add_legend adds a second legend without removing the default one.

The examples on this page: http://cosmologist.info/cosmomc/readme_python.html don't seem to have this problem. I noticed they were made using planckStyle.getSinglePlotter. Unfortunately importing planckStyle throws up some latex-related error on the machine where my chains live, so I was wondering if there was another way.

Antony Lewis
Posts: 1659
Joined: September 23 2004
Affiliation: University of Sussex
Contact:

### Re: GetDist: removing legend

Those examples are using plot_2D to built up the plot, not the automatic plots_2D.

You can also use

Code: Select all

g.settings.line_labels =False

Posts: 33
Joined: July 17 2014
Affiliation: Johns Hopkins University

### GetDist: removing legend

Ah OK, hadn't appreciated the plot_2D vs plots_2D. Thanks.


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# Thread: Right Hand Riemann sum with n=5 (CalcII)

1. ## Right Hand Riemann sum with n=5 (CalcII)

I'm STUCK and can't solve part b
ptb.jpg
I'm getting a negative number for the answer and it's wrong.

2. We can't tell what you are, or are not, doing wrong if you don't tell us what you did!

The problem says to use the "Right Hand Riemann Sum". So what are the correct values of x and g'(x) at each point? What is $\Delta x$? You will need, of course, to multiply $x^2$, $g'(x)$, and $\Delta x$ for each interval, then add.

I don't see how you get a negative answer when every number you are given is positive!


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# Documentation of the frequently-used functions in the context of Internal and Compile [duplicate]

In this site, I have gradually found out that there is a collection of functions in all kinds of answers. For instance, CompileGetElement, InternalBag,etc. However, these functions are undocumented.

• CompileGetElement see this post

• InternalBag see this post

For example

 ?Compile*

 ?Internal*

### Question

Is it possible to have a documentation of these frequently-used functions with the help of the users in this community?

Like the styles as follow

For example, using the following style:

• function[arg1,arg2] realize the functionality ....
• Basic Usage Example
• @ShutaoTang It seems to me that this question could be merged with (805). Would you make the case for not merging these, please? – Mr.Wizard May 14 '15 at 15:45
• @Mr.Wizard, OK, but I don't know how to merge it to (805). So please help me do this operation.:) – xyz May 15 '15 at 3:21
• @Mr.Wizard, Yes, now I think my question indeed is a subset of question(805) that you linked. – xyz May 15 '15 at 3:34
• @ShutaoTang After the bounty has completed I shall close the question as a duplicate and post in Chat; if no one objects I shall perform the (irreversible) Merge to move the answers from this question to the master and redirect users to that question. – Mr.Wizard May 15 '15 at 4:23


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# Open Problems in Mathematical Systems and Control Theory

## Paperback(Softcover reprint of the original 1st ed. 1999)

\$149.99
Choose Expedited Shipping at checkout for guaranteed delivery by Thursday, February 21

## Product Details

ISBN-13: 9781447112075 Springer London 10/12/2011 Communications and Control Engineering Softcover reprint of the original 1st ed. 1999 289 6.10(w) x 9.25(h) x 0.03(d)

1 Uniform asymptotic stability of linear time-varying systems.- 2 Positive system realizations.- 3 System approximation in the 2-norm.- 4 Is it possible to recognize local controllability in a finite number of differentiations?.- 5 Open problems in sequential parametric estimation.- 6 Conditions for the existence and uniqueness of optimal matrix scalings.- 7 Matrix inequality conditions for canonical factorization of rational transfer function matrices.- 8 Open problems in ?1 optimal control.- 9 Efficient neural network learning.- 10 Mechanical feedback control systems.- 11 Three problems on the decidability and complexity of stability.- 12 Simultaneous stabilization of linear systems and interpolation with rational functions.- 13 Forbidden state control synthesis for timed Petri net models.- 14 On matrix mortality in low dimensions.- 15 Entropy and random feedback.- 16 A stabilization problem.- 17 Spectral factorization of a spectral density with arbitrary delays.- 18 Lyapunov exponents and robust stabilization.- 19 Regular spectral factorizations.- 20 Convergence of an algorithm for the Riemannian SVD.- 21 Some open questions related to flat nonlinear systems.- 22 Approximation of complex ?.- 23 Spectral value sets of infinite-dimensional systems.- 24 Selection of the number of inputs and states.- 25 Input-output gains of switched linear systems.- 26 Robust stability of linear stochastic systems.- 27 Monotonicity of performance with respect to its specification in H? control.- 28 Stable estimates in equation error identification: An open problem.- 29 Elimination of latent variables in real differential algebraic systems.- 30 How conservative is the circle criterion?.- 31 On chaotic observer design.- 32 The minimal realization problem in the max-plus algebra.- 33 Input design for worst-case identification.- 34 Max-plus-times linear systems.- 35 Closed-loop identification and self-tuning.- 36 To estimate the L2-gain of two dynamic systems.- 37 Open problems in the area of pole placement.- 38 An optimal control theory for systems defined over finite rings.- 39 Re-initialization in discontinuous systems.- 40 Control-Lyapunov functions.- 41 Spectral Nevanlinna-Pick Interpolation.- 42 Phase-sensitive structured singular value.- 43 Conservatism of the standard upper bound test: Is sup$$\left( {\bar \mu /\mu } \right)$$ finite? Is it bounded by 2?.- 44 When does the algebraic Riccati equation have a negative semi-definite solution?.- 45 Representing a nonlinear input-output differential equation as an input-state-output system.- 46 Shift policies in QR-like algorithms and feedback control of self-similar flows.- 47 Equivalences of discrete-event systems and of hybrid systems.- 48 Covering numbers for input-output maps realizable by neural networks.- 49 A powerful generalization of the Carleson measure theorem?.- 50 Lyapunov theory for high order differential systems.- 51 Performance lower bound for a sampled-data signal reconstruction.- 52 Coprimeness of factorizations over a ring of distributions.- 53 Where are the zeros located?.


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1

# Why does my NPC stop moving when its on the players head?

Edited 5 months ago

The pathfinding script works ok, the big problem is that if the Player jumps on the bot's head, it will just stand still and do nothing. This also happens if the bot ends up on the players head. This is the script.

A gif of what happens when the player is on the bots head: https://gyazo.com/658f26c3f213198bb93d6d1b13c7291f

Another gif of the bot attacking the player when its on ground, but doing nothing when the player manages to jump onto its head: https://gyazo.com/d45ef6a021ed9a01ceaea612c13ce6f0

function findNearestTorso(pos)
local list = game.Workspace:children()
local torso = nil
local dist = 1000
local temp = nil
local human = nil
local temp2 = nil
for x = 1, #list do
temp2 = list[x]
if (temp2.className == "Model") and (temp2 ~= script.Parent) then
if string.match(temp2.Name, "Dummy") or string.match(temp2.Name, "dummy") or string.match(temp2.Name, "Gyro") then else
temp = temp2:findFirstChild("Torso")
human = temp2:findFirstChild("Humanoid")
if (temp ~= nil) and (human ~= nil) and (human.Health > 0) then
if (temp.Position - pos).magnitude < dist then
torso = temp
dist = (temp.Position - pos).magnitude
end
end
end
end
end
end

coroutine.resume(coroutine.create(function()
print("the walk coroutine started")
while true do
wait(0.3)
local target = findNearestTorso(script.Parent.Torso.Position)
if target ~= nil then
script.Parent.Humanoid:MoveTo(target.Position, target)
end
end
end))

How can I edit this script so that when the player ends up on the bots head, the bot will adjust itself until the player falls off the bots head? I also want this to happen if the bot ends up on the players head. Any and all help is appreciated.

0

Taking a quick glance at the problem and script I can take a guess at what your trying to achieve.

while wait(1) do
-- make a new path with math.random. I would recommend using Humanoid.Jump as well.
wait(0.5)