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#1 Re: Coder's Corner » Trying to approximate factorial function using Lagrange polynomial » 2024-09-19 03:29:07

Factorial Calculation: Ensure that your factorial function handles large numbers correctly. Factorials grow very quickly, so you might need to use a more robust data type or library that supports arbitrary-precision arithmetic if your current setup isn't handling large numbers well.

Interpolation Points: The values you're using for interpolation (100!, 200!, 300!, 400!) are extremely large. Make sure the Lagrange polynomial is being computed with correct precision and that you handle very large numbers properly in your calculations.

Lagrange Polynomial Calculation: Double-check the polynomial formula and make sure the indices and arithmetic operations are correctly implemented. Small mistakes in the formula can lead to large discrepancies in the results.

Testing and Debugging: You might want to test with smaller, more manageable numbers to ensure that your polynomial interpolation is working as expected before scaling up to large factorials.

#2 Re: Coder's Corner » Technical Coding » 2024-09-16 04:15:47

Maybe this code will help you:
#include <stdio.h>
#include <time.h>
#include <stdlib.h>

// Function prototypes
void displayCharacter(int keyPressCount, int key);
void clearScreen();
char getCharacter(int key, int pressCount);

int main() {
    int key;
    int keyPressCount = 0;
    time_t lastPressTime = 0;
    time_t currentTime;
    const int delay = 2;  // Time delay in seconds to distinguish key presses

    while (1) {
        printf("Enter a key (2-9) or 0 to exit: ");
        key = getchar();
        while (getchar() != '\n'); // Clear input buffer

        if (key == '0') {
            break;
        }

        currentTime = time(NULL);

        if (key >= '2' && key <= '9') {
            if (key == lastPressTime) {
                if (difftime(currentTime, lastPressTime) < delay) {
                    keyPressCount++;
                } else {
                    keyPressCount = 1;
                }
            } else {
                keyPressCount = 1;
            }

            lastPressTime = key;
            clearScreen();
            displayCharacter(keyPressCount, key - '0');
        } else {
            printf("Invalid key. Press a number between 2 and 9.\n");
        }
    }

    return 0;
}

void displayCharacter(int keyPressCount, int key) {
    char character = getCharacter(key, keyPressCount);
    printf("%c\n", character);
}

char getCharacter(int key, int pressCount) {
    char result = '\0';

    switch (key) {
        case 2: result = "ABC"[pressCount - 1]; break;
        case 3: result = "DEF"[pressCount - 1]; break;
        case 4: result = "GHI"[pressCount - 1]; break;
        case 5: result = "JKL"[pressCount - 1]; break;
        case 6: result = "MNO"[pressCount - 1]; break;
        case 7: result = "PQRS"[pressCount - 1]; break;
        case 8: result = "TUV"[pressCount - 1]; break;
        case 9: result = "WXYZ"[pressCount - 1]; break;
    }

    return result;
}

void clearScreen() {
    system("CLS");
}

#3 Re: Coder's Corner » Regression Analysis - Constructing a model with constraints -Help Plz » 2023-06-15 00:37:21

To construct a general function/method based on two sets of minimum/maximum data point constraints with a fixed starting point (y-axis intercept) and x-range, while maintaining a zero rate of change over time, you can use a piecewise linear function. This approach will allow you to vary the function as little as possible within the given constraints.

Here's an outline of the method:

Define your starting point, which is the y-axis intercept.
Determine the x-range over which your function will operate.
Divide the x-range into segments based on the constraints you have. Each segment will correspond to a different rate of change.
Calculate the slope for each segment, which represents the rate of change for that segment. The slope should be such that the segment stays within the given minimum and maximum constraints.
Create linear equations for each segment using the slope and the corresponding x-values.
Combine all the linear equations to form a piecewise function that satisfies the constraints and has zero overall rate of change.
Here's an example to illustrate the process:

Let's say we have the following constraints:

Starting point: (0, 10)
x-range: [0, 10]
Minimum constraint: (2, 8)
Maximum constraint: (8, 12)
To construct the function, we'll divide the x-range into two segments: [0, 2] and [2, 10].

For the first segment, the slope can be calculated as:
slope1 = (8 - 10) / (2 - 0) = -1

The linear equation for the first segment becomes:
y1 = -x + 10

For the second segment, the slope can be calculated as:
slope2 = (12 - 8) / (8 - 2) = 0.8

The linear equation for the second segment becomes:
y2 = 0.8x + 2.4

Combining both segments, we get the piecewise function:
f(x) = {
-x + 10, 0 <= x <= 2
0.8x + 2.4, 2 < x <= 10
}

This function satisfies the constraints, has zero overall rate of change, and varies as little as possible within the given constraints.

You can modify this approach to accommodate different sets of constraints by adjusting the number of segments and calculating the slopes accordingly.

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