com.seeq.model.RegressionOutputV1 Maven / Gradle / Ivy
/*
* Seeq REST API
* No description provided (generated by Swagger Codegen https://github.com/swagger-api/swagger-codegen)
*
* OpenAPI spec version: 60.1.3-v202304250417
*
*
* NOTE: This class is auto generated by the swagger code generator program.
* https://github.com/swagger-api/swagger-codegen.git
* Do not edit the class manually.
*/
package com.seeq.model;
import java.util.Objects;
import java.util.Arrays;
import com.fasterxml.jackson.annotation.JsonProperty;
import com.fasterxml.jackson.annotation.JsonCreator;
import com.fasterxml.jackson.annotation.JsonValue;
import io.swagger.v3.oas.annotations.media.Schema;
/**
* Regression output from the formula result. Note that the `table` will also contain values.
*/
@Schema(description = "Regression output from the formula result. Note that the `table` will also contain values.")
public class RegressionOutputV1 {
@JsonProperty("adjustedRSquared")
private Double adjustedRSquared = null;
@JsonProperty("errorSumSquares")
private Double errorSumSquares = null;
@JsonProperty("intercept")
private Double intercept = null;
@JsonProperty("interceptStandardError")
private Double interceptStandardError = null;
@JsonProperty("isUncertain")
private Boolean isUncertain = false;
@JsonProperty("rSquared")
private Double rSquared = null;
@JsonProperty("regressionSumSquares")
private Double regressionSumSquares = null;
@JsonProperty("suggestedPValueCutoff")
private Double suggestedPValueCutoff = null;
public RegressionOutputV1 adjustedRSquared(Double adjustedRSquared) {
this.adjustedRSquared = adjustedRSquared;
return this;
}
/**
* The measure of how close the data is to the regression line, adjusted for the number of input signals and samples
* @return adjustedRSquared
**/
@Schema(required = true, description = "The measure of how close the data is to the regression line, adjusted for the number of input signals and samples")
public Double getAdjustedRSquared() {
return adjustedRSquared;
}
public void setAdjustedRSquared(Double adjustedRSquared) {
this.adjustedRSquared = adjustedRSquared;
}
public RegressionOutputV1 errorSumSquares(Double errorSumSquares) {
this.errorSumSquares = errorSumSquares;
return this;
}
/**
* The standard error for the sum squares
* @return errorSumSquares
**/
@Schema(required = true, description = "The standard error for the sum squares")
public Double getErrorSumSquares() {
return errorSumSquares;
}
public void setErrorSumSquares(Double errorSumSquares) {
this.errorSumSquares = errorSumSquares;
}
public RegressionOutputV1 intercept(Double intercept) {
this.intercept = intercept;
return this;
}
/**
* The constant offset to add. 0 if forceThroughZero was true. This is the intercept for the output signal rather than the individual coefficients.
* @return intercept
**/
@Schema(required = true, description = "The constant offset to add. 0 if forceThroughZero was true. This is the intercept for the output signal rather than the individual coefficients.")
public Double getIntercept() {
return intercept;
}
public void setIntercept(Double intercept) {
this.intercept = intercept;
}
public RegressionOutputV1 interceptStandardError(Double interceptStandardError) {
this.interceptStandardError = interceptStandardError;
return this;
}
/**
* The standard error for the intercept
* @return interceptStandardError
**/
@Schema(required = true, description = "The standard error for the intercept")
public Double getInterceptStandardError() {
return interceptStandardError;
}
public void setInterceptStandardError(Double interceptStandardError) {
this.interceptStandardError = interceptStandardError;
}
public RegressionOutputV1 isUncertain(Boolean isUncertain) {
this.isUncertain = isUncertain;
return this;
}
/**
* True if this regression is uncertain
* @return isUncertain
**/
@Schema(required = true, description = "True if this regression is uncertain")
public Boolean getIsUncertain() {
return isUncertain;
}
public void setIsUncertain(Boolean isUncertain) {
this.isUncertain = isUncertain;
}
/**
* The measure of how close the data is to the regression line
* @return rSquared
**/
@Schema(description = "The measure of how close the data is to the regression line")
public Double getRSquared() {
return rSquared;
}
public RegressionOutputV1 regressionSumSquares(Double regressionSumSquares) {
this.regressionSumSquares = regressionSumSquares;
return this;
}
/**
* The measure of how well the model matches the target
* @return regressionSumSquares
**/
@Schema(required = true, description = "The measure of how well the model matches the target")
public Double getRegressionSumSquares() {
return regressionSumSquares;
}
public void setRegressionSumSquares(Double regressionSumSquares) {
this.regressionSumSquares = regressionSumSquares;
}
public RegressionOutputV1 suggestedPValueCutoff(Double suggestedPValueCutoff) {
this.suggestedPValueCutoff = suggestedPValueCutoff;
return this;
}
/**
* The value which the regression method suggests for ignoring coefficients
* @return suggestedPValueCutoff
**/
@Schema(required = true, description = "The value which the regression method suggests for ignoring coefficients")
public Double getSuggestedPValueCutoff() {
return suggestedPValueCutoff;
}
public void setSuggestedPValueCutoff(Double suggestedPValueCutoff) {
this.suggestedPValueCutoff = suggestedPValueCutoff;
}
@Override
public boolean equals(java.lang.Object o) {
if (this == o) {
return true;
}
if (o == null || getClass() != o.getClass()) {
return false;
}
RegressionOutputV1 regressionOutputV1 = (RegressionOutputV1) o;
return Objects.equals(this.adjustedRSquared, regressionOutputV1.adjustedRSquared) &&
Objects.equals(this.errorSumSquares, regressionOutputV1.errorSumSquares) &&
Objects.equals(this.intercept, regressionOutputV1.intercept) &&
Objects.equals(this.interceptStandardError, regressionOutputV1.interceptStandardError) &&
Objects.equals(this.isUncertain, regressionOutputV1.isUncertain) &&
Objects.equals(this.rSquared, regressionOutputV1.rSquared) &&
Objects.equals(this.regressionSumSquares, regressionOutputV1.regressionSumSquares) &&
Objects.equals(this.suggestedPValueCutoff, regressionOutputV1.suggestedPValueCutoff);
}
@Override
public int hashCode() {
return Objects.hash(adjustedRSquared, errorSumSquares, intercept, interceptStandardError, isUncertain, rSquared, regressionSumSquares, suggestedPValueCutoff);
}
@Override
public String toString() {
StringBuilder sb = new StringBuilder();
sb.append("class RegressionOutputV1 {\n");
sb.append(" adjustedRSquared: ").append(toIndentedString(adjustedRSquared)).append("\n");
sb.append(" errorSumSquares: ").append(toIndentedString(errorSumSquares)).append("\n");
sb.append(" intercept: ").append(toIndentedString(intercept)).append("\n");
sb.append(" interceptStandardError: ").append(toIndentedString(interceptStandardError)).append("\n");
sb.append(" isUncertain: ").append(toIndentedString(isUncertain)).append("\n");
sb.append(" rSquared: ").append(toIndentedString(rSquared)).append("\n");
sb.append(" regressionSumSquares: ").append(toIndentedString(regressionSumSquares)).append("\n");
sb.append(" suggestedPValueCutoff: ").append(toIndentedString(suggestedPValueCutoff)).append("\n");
sb.append("}");
return sb.toString();
}
/**
* Convert the given object to string with each line indented by 4 spaces
* (except the first line).
*/
private String toIndentedString(java.lang.Object o) {
if (o == null) {
return "null";
}
return o.toString().replace("\n", "\n ");
}
}